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Listen to the article 6 min
Anthony Hamlet was superintendent of Pittsburgh Public Schools from 2016 through 2021.
The past two years have offered ample evidence of the impact remote learning has on students. Virtual education systems kept schools running safely during the early days of the pandemic, reduced bullying in some cases, and gave many students a new sense of autonomy.

But as we returned to classrooms, other effects became apparent. While remote, students missed hands-on learning opportunities and time to bond with peers. Teachers struggled to connect with at-risk students, many of whom had difficulty focusing on their studies in unstable homes. Across the board, students missed educational milestones, falling behind in math, reading and science courses.

School administrators and staff worked diligently to adopt online learning practices and distributed laptops to students in record time. Still, some students had unreliable access to Wi-Fi, others lost loved ones to COVID-19, and many older teens picked up jobs to support their households. These circumstances worsened mental health outcomes and ultimately disengaged students from their lessons.

Student engagement was already alarmingly low prior to the pandemic. Out of 5 million grade 5-12 students surveyed by Gallup in 2018, 29% reported being "not engaged," and another 24% said they felt "actively disengaged" in learning. The survey further indicated engagement decreases as students age, with only one-third of high schoolers feeling "highly engaged" at school.

Motivation and morale dropped further in the 2020-21 school year, according to data from the EdWeek Research Center. Approximately half of students reported feeling less motivated in class and experiencing lower morale. Teachers' responses were more dire, estimating more than 80% of their pupils were less motivated and had lower morale.

Student engagement is at the core of successful outcomes in schools, whether that be measured in test scores, social-emotional development or graduation rates. So how can educators meet students where they are? How do we course-correct after months of missed learning opportunities and delays?

Methods to reengage students

There's no single answer. Demonstrating our commitment to students will take multiple forms and require open dialogue, trust and empathy. Below are a few practices for teachers and administrators to consider.

No two students are identical learners. The following suggestions could have varying success rates depending on classroom size, education level and students' backgrounds. But with a little personalization, practices like student ownership and hybrid learning can boost engagement.

Encourage collaboration

Being back in the classroom means students reuniting with their peers. Rather than allow such socialization to distract from course materials, teachers can harness that energy. After time apart, group projects and paired learning opportunities serve a dual purpose: encouraging teamwork and reestablishing social norms.

It's okay to start small. Many teachers report success with " Think-Pair-Share." This practice has students reflect on what they learned, discuss it with a classmate, and then share their conclusions with the entire class. A five- to 10-minute activity, Think-Pair-Share shakes up the learning day and nudges students to internalize their lessons.

Prompt students to take ownership

It's been a long time since a class schedule consisted of hour-long lectures and assigned readings. Nowadays, education experts and teachers understand students need greater stimulation to avoid drifting off mid-lesson. Thus, classrooms are an ideal place to support the autonomy of young people who are beginning to recognize and test their limits.

In planning a lesson, make room for student choice. If students need to demonstrate their grasp of how ecosystems function, offer supplies for an array of final projects: a presentation, a written report on a local ecosystem or a self-built terrarium.

Allowing students to make choices tells them educators care about their opinions. It also prompts self-assessment. Students will ask themselves which project they will excel at, which might be a challenge and, moreover, which speaks to their own interests.

Infuse a real-world impact into lessons

We want our classrooms to be a reliable space for students to learn and grow without outside stressors. However, creating a safe environment is not the same as dismissing lived experiences. In fact, connecting students' studies to their everyday lives often helps them see why they matter in the first place.

In social studies, that might look like initiating a cultural appreciation day, where students discuss their family history, traditions and cuisine. In a literature course, students could write letters to their representatives or create stories for folks in local retirement homes.

If a school has the space and means, science students could build a community garden. School gardens create year-round learning opportunities focused on the local climate, economy and various agricultural skills. By the end of the school year, students receive tangible evidence of their hard work and a new way to engage with the world around them.

Offer sustained virtual opportunities

The pandemic continues to impact our students. But after quickly adapting to remote learning, educators learned new ways to stay in touch with students outside the classroom. Virtual learning shouldn't lapse because we are back, because online communication is crucial to sustained engagement.

When it comes to study habits, incorporating online programs like Quizlet and Kahoot!into lessons will make students realize being at home doesn't automatically equate to learning loss. Similar collaborative homework methods can transform a necessary task into a fun extension of any lesson. At the same time, administrators can engage parents and guardians through frequent newsletters and announcements. Informed adults at home can further encourage engaged students.

Conduct engagement check-ins

At the end of the day, the best way to measure engagement is to ask students directly. An anonymous online poll each week could provide insight into what is working in a particular lesson and what needs improvement. More often than not, these constructive critiques go unspoken.

But in the same vein as student ownership, requesting student input offers them a sense of control. Young people want to be heard, to be listened to. Finding small ways to accept feedback and make (reasonable) changes could mean the difference between a committed learner and one simply going through the motions.

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5 strategies for rebuilding student engagement after COVID-19

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The former superintendent of Pittsburgh Public Schools writes that reconnecting requires multiple strategies, open dialogue, trust and empathy.

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Project-Based Learning (PBL)

Teachers can use this approach to project-based learning to help ensure that the final product comes from truly engaged learning.

Sustaining student engagement throughout a project-based learning (PBL) unit is a common challenge for classroom practitioners. When a project begins, engagement starts off high, thanks to the natural curiosity provoked by a project launch that includes elements like interesting questions or multimedia resources, as well as the interest that comes with a fresh endeavor.

However, this honeymoon period doesn't last long as students begin to realize that project work requires effort. This sometimes leads to a drop in interest and engagement, at least until a few days before the conclusion of the project, when students scramble to put the finishing touches on their work ahead of the due date. Outside of the beginning and end, the middle portions of a project can sometimes feel like a struggle, which is why the middle of a PBL project is often called "the messy middle," because maintaining student interest is a challenge even for experienced PBL teachers.

This gradual slide into the messy middle isn't inevitable. There are several strategies that teachers can employ to reinvigorate student interest and reengage them with the project process, including one I call the surprise, rehook, engage (SRE) method.

STEP 1: SURPRISE

Classroom norms, routines, and predictable structures often form the backbone of highly effective PBL classrooms, so it may seem counterintuitive to break them, but that's just what I'm advising you to do. When you see your students struggling to stay engaged with whatever part of the project they're working on, it's time to recapture their interest. The surest way to do that is to do something outside of the norm in your classroom. The more consistent you are with your classroom expectations and routines, the more surprised your students will be when you abandon them, and it's this surprise factor that will help bring them back onto the project path.

Surprise is a biological response to an unexpected stimulus, something that our primitive ancestors needed to survive the dangers that filled the harsh primeval world they lived in. This natural alarm system is still buried deep in our reptilian brains, forcing us to pay attention when a squirrel runs across the road in front of our car or when a psychotic killer bursts out of a closet during our favorite scary movie.

If you surprise your students, they will be biologically compelled to focus on you, granting you their attention once again. There are many things that you can do to capitalize on the power of surprise: changing the physical layout of the classroom, introducing a new protocol or process, teaching a lesson embedded with multimedia resources, or even arriving to class dressed in a costume or equipped with tactical learning resources or artifacts. If it's something they're not used to seeing in your room, all the better, but once you capture their attention, you need to move to the second part of the strategy quickly because the power of surprise dissipates over time.

Step 2: Rehook

PBL promotes rigorous problem-solving and critical thinking when done well, but these kinds of processes can tax even the most dedicated student over time, causing them to disengage or give up when they've reached their limit and left the zone of proximal development. Student disengagement or lackluster performance can sometimes indicate that they've just hit their limit, and the antidote could be helping to rebuild their confidence by granting them an easy win.

Once you've rehooked your students' attention, possibly by leveraging the power of surprise, you will immediately want to move into an activity or structured lesson designed to be accessible and easy to understand. Providing your students with an academic win can help rebuild sapped confidence and reengage them with the project path.

There are all sorts of activities you can employ that have a light academic load but are still valuable to learning; a review game, a choice-based stations activity, a highly structured discussion, or a simple partner activity are all good candidates. One approach I recommend considering is the visible thinking routines championed by Harvard's Project Zero. These highly scaffolded academic processes are universal in their accessibility, meaning that they work just as well for kindergartners as they do for MIT graduate students. Whatever activity you choose, make sure that it acts as a bridge back to the project path you originally laid out in your project plan.

Step 3: Engage

While reengagement strategies often prove fruitful, there are no silver bullets in education, and as such, it's important to make sure that you have more than one reengagement strategy at the ready. If your attempts to reengage your students with confidence-building tasks don't work, another option that can be used either in its place or directly after is a questioning activity. Questions fuel the inquiry processes that help drive the self-directed learning in a project-based classroom, and if your students ever run out of questions or wonder about the relevance of the ones they've already brainstormed, it may be time to refresh their lists.

To do this, you'll want to relaunch the project-but on a small scale so that you don't lose much time. Facilitate a new entry event that provokes curiosity or gets students thinking about what comes next, and then move quickly into a question-formulation process to refresh the avenues of inquiry open to them. Again, this should be able to be completed in a single instructional period or hour; a method such as a gallery walk, a question formulation (QFT) protocol, or a short small group discussion will get you what you need without a lot of delay.

So, the next time you feel your class straying from your carefully designed project path and slipping into the valley of the messy middle, remember the SRE strategy as a way to get them back on track quickly and effectively.

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A 3 Step Strategy for Sustaining Student Engagement in PBL

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Teachers can use this approach to project-based learning to help ensure that the final product comes from truly engaged learning.

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Department of Mathematics, University of California, San Diego, San Diego, CA, United States

Halıcıoǧlu Data Science Institute, University of California, San Diego, San Diego, CA, United States

Joint Doctoral Program in Math and Science Education, San Diego State University, San Diego, CA, United States

Joint Doctoral Program in Math and Science Education, University of California, San Diego, San Diego, CA, United States

Department of Physical Therapy, Movement and Rehabilitation Science, Bouvé College of Health, Northeastern University, Boston, MA, United States

Art and Design, College of Arts, Media and Design, Northeastern University, Boston, MA, United States

Qualcomm Institute, University of California, San Diego, San Diego, CA, United States

The COVID-19 pandemic resulted in nearly all universities switching courses to online formats. We surveyed the online learning experience of undergraduate students ( n = 187) at a large, public research institution in course structure, interpersonal interaction, and academic resources. Data was also collected from course evaluations. Students reported decreases in live lecture engagement and attendance, with 72 percent reporting that low engagement during lectures hurt their online learning experience. A majority of students reported that they struggled with staying connected to their peers and instructors and managing the pace of coursework. Students had positive impressions, however, of their instructional staff. Majorities of students felt more comfortable asking and answering questions in online classes, suggesting that there might be features of learning online to which students are receptive, and which may also benefit in-person classes.

Introduction

In Spring 2020, 90% of higher education institutions in the United States canceled in-person instruction and shifted to emergency remote teaching (ERT) due to the COVID-19 pandemic ( Lederman, 2020). ERT in response to COVID-19 is qualitatively different from typical online learning instruction as students did not self-select to participate in ERT and teachers were expected to transition to online learning in an unrealistic time frame ( Brooks et al., 2020; Hodges et al., 2020; Johnson et al., 2020). This abrupt transition left both faculty and students without proper preparation for continuing higher education in an online environment.

In a random sample of 1,008 undergraduates who began their Spring 2020 courses in-person and ended them online, 51% of respondents said they were very satisfied with their course before the pandemic, and only 19% were very satisfied after the transition to online learning ( Means and Neisler, 2020). Additionally, 57% of respondents said that maintaining interest in the course material was "worse online," 65% claimed they had fewer opportunities to collaborate with peers, and 42% said that keeping motivated was a problem ( Means and Neisler, 2020). Another survey of 3,089 North American higher education students had similar results with 78% of respondents saying online experiences were not engaging and 75% saying they missed face-to-face interactions with instructors and peers ( Read, 2020). Lastly, of the 97 university presidents surveyed in the United States by Inside Higher Ed, 81% claimed that maintaining student engagement would be challenging when moving classes online due to COVID-19 ( Inside Higher Ed, 2020).

In this report, we consider the measures and strategies that were implemented to engage students in online lectures at UCSD during ERT due to the COVID-19 pandemic. We investigate student perceptions of these measures and place our findings in the larger context of returning to in-person instruction and improving engagement in both online and in-person learning for undergraduates. Before diving into the current study, we first define what we mean by engagement.

Theoretical Framework and Literature Review

Student Engagement

Student engagement has three widely accepted dimensions: behavioral, cognitive and affective ( Chapman, 2002; Fredricks et al., 2004, 2016; Mandernach, 2015). Each dimension has indicators ( Fredricks et al., 2004), or facets ( Coates, 2007), that manifest each dimension. Behavioral engagement refers to active responses to learning activities and is indicated by participation, persistence, and/or positive conduct. Cognitive engagement includes mental effort in learning activities and is indicated by deep learning, self-regulation, and understanding. Affective engagement is the emotional investment in learning activities and is indicated by positive reactions to the learning environment, peers, and teachers as well as a sense of belonging. A list of indicators for each dimension can be found in Bond et al. (2020).

The literature also theorizes different influences for each engagement dimension. Most influencing factors are sociocultural in nature and can include the political, social, and teaching environment as well as relationships within the classroom ( Kahu, 2013). In particular, social engagement with peers and instructors creates a sense of community, which is often correlated with more effective learning outcomes ( Rovai and Wighting, 2005; Liu et al., 2007; Lear et al., 2010; Kendricks, 2011; Redmond et al., 2018; Chatterjee and Correia, 2020). Three key classroom interactions are often investigated when trying to understand the factors influencing student engagement: student-student interactions, student-instructor interactions, and student-content interactions ( Moore, 1993).

Student-student interactions prevent boredom and isolation by creating a dynamic sense of community ( Martin and Bolliger, 2018). Features that foster student-student interactions in online learning environments include group activities, peer assessment, and use of virtual communication spaces such as social media, chat forums, and discussion boards ( Revere and Kovach, 2011; Tess, 2013; Banna et al., 2015). In the absence of face-to-face communication, these virtual communication spaces help build student relationships ( Nicholson, 2002; Harrell, 2008). In a survey of 1,406 university students in asynchronous online courses, the students claimed to have greater satisfaction and to have learned more when more of the course grade was based on discussions, likely because discussions fostered increased student-student and student-instructor interactions ( Shea et al., 2001). Interestingly, in another study, graduate students in online courses claimed that student-student interactions were the least important of the three for maintaining student engagement, but that they were more likely to be engaged if an online course had online communication tools, ice breakers, and group activities ( Martin and Bolliger, 2018).

In the Martin and Bolliger (2018) study, the graduate students enrolled in online courses found student-instructor interactions to be the most important of the three interaction types, which supports prior work that found students perceive student-instructor interactions as more important than peer interactions in fostering engagement ( Swan and Shih, 2005). Student-instructor interactions increased in frequency in online classes when the following practices were implemented (1) multiple open communication channels between students and instructors ( Gaytan and McEwen, 2007; Dixson, 2010; Martin and Bolliger, 2018), (2) regular communication of announcements, reminders, grading rubrics, and expectations by instructors ( Martin and Bolliger, 2018), (3) timely and consistent feedback provided to students ( Gaytan and McEwen, 2007; Dixson, 2010; Chakraborty and Nafukho, 2014; Martin and Bolliger, 2018), and (4) instructors taking a minimal role in course discussions ( Mandernach et al., 2006; Dixson, 2010).

Student-content interactions include any interaction the student has with course content. Qualities that have been shown to increase student engagement with course content include the use of curricular materials and classroom activities that incorporate realistic scenarios, prompts that scaffold deep reflection and understanding, multimedia instructional materials, and those that allow student agency in choice of content or activity format ( Abrami et al., 2012; Wimpenny and Savin-Baden, 2013; Britt et al., 2015; Martin and Bolliger, 2018). In online learning, students need to be able to use various technologies in order to be able to engage in student-content interactions, so technical barriers such as lack of access to devices or reliable internet can be a substantial issue that deprives educational opportunities especially for students from lower socioeconomic households ( Means and Neisler, 2020; Reich et al., 2020; UNESCO, 2020).

Engagement in Online Learning

Bond and Bedenlier (2019) present a theoretical framework for engagement in online learning that combines the three dimensions of engagement, types of interactions that can influence the engagement dimensions, and possible short term and long term outcomes. The types of interactions are based on components present in the student's immediate surrounding or microsystem, and are largely based on Moore's three types of interactions: teachers, peers, and curriculum. However, the authors add technology and the classroom environment as influential components because they are particularly important for online learning.

Specific characteristics of each microsystem component can differentially modulate student engagement, and each component has at least one characteristic that specifically focuses on technology. Teacher presence, feedback, support, time invested, content expertise, information and communications technology skills and knowledge, technology acceptance, and use of technology all can influence the types of interactions students might have with their teachers which would then impact their engagement ( Zhu, 2006; Beer et al., 2010; Zepke and Leach, 2010; Ma et al., 2015; Quin, 2017). For curriculum/activities, the quality, design, difficulty, relevance, level of required collaboration, and use of technology can influence the types of interactions a student might encounter that could impact their engagement ( Zhu, 2006; Coates, 2007; Zepke and Leach, 2010; Bundick et al., 2014; Almarghani and Mijatovic, 2017; Xiao, 2017). Characteristics that can change the quantity and quality of peer interactions and thereby influence engagement include the amount of opportunities to collaborate, formation of respectful relationships, clear boundaries and expectations, being able to physically see each other, and sharing work with others and in turn respond to the work of others ( Nelson Laird and Kuh, 2005; Zhu, 2006; Yildiz, 2009; Zepke and Leach, 2010). When describing influential characteristics, the authors combine classroom environment and technology because in online learning, the classroom environment inherently utilizes technology. The influential characteristics of these two components are access to technology, support in using and understanding technology, usability, design, technology choice, sense of community, and types of assessment measures. All of these characteristics demonstrably influenced engagement levels in prior literature ( Zhu, 2006; Dixson, 2010; Cakir, 2013; Levin et al., 2013; Martin and Bolliger, 2018; Northey et al., 2018; Sumuer, 2018).

Online learning can take place in different formats, including fully synchronous, fully asynchronous, or blended ( Fadde and Vu, 2014). Each of these formats offers different challenges and opportunities for technological ease, time management, community, and pacing. Fully asynchronous learning is time efficient, but offers less opportunity for interactions that naturally take place in person ( Fadde and Vu, 2014). Instructors and students may feel underwhelmed by the lack of immediate feedback that can happen in face to face class time ( Fadde and Vu, 2014). Synchronous online learning is less flexible for teachers and students and requires reliable technology, but allows for more real time engagement and feedback ( Fadde and Vu, 2014). In blended learning courses, instructors have to coordinate and organize both the online and in person meetings and lessons, which is not as time efficient. Blended learning means there is some in person engagement which provides spontaneity and more natural personal relations ( Fadde and Vu, 2014). In all online formats, students may feel isolated and instructors and students need to spend more time and intention into building community ( Fadde and Vu, 2014; Gillett-Swan, 2017). Often, instructors can use learning management systems and discussion boards to help facilitate student interaction and connection ( Fadde and Vu, 2014). In terms of group work, engagement and participation is dependent not only on the modality of learning, but also the instructors expectations for assessment ( Gillett-Swan, 2017). Given the flexibility and power of online meeting and work environments, collaborating synchronously or asynchronously are both possible and effective ( Gillett-Swan, 2017). In online learning courses, especially fully asynchronous, students are more accountable for their learning, which may be challenging for students who struggle with self-regulating their work pace ( Gillett-Swan, 2017). Learning from home also means there are more distractions than when students attend class on campus. At any point during class, children, pets, or work can interrupt a student's, or instructor's, remote learning or teaching ( Fadde and Vu, 2014).

According to Raes et al. (2019), the flexibility of a blended -or hybrid- learning environment encourages more students to show up to class when they otherwise would have taken a sick day, or would not have been able to attend due to home demands. It also equalizes learning opportunities for underrepresented groups, and more comprehensive support with two modes of interaction. On the other hand, hybrid learning can cause more strain on the instructor who may have to adapt their teaching designs for the demands of this unique format while maintaining the same standards ( Bülow, 2022). Due to the nature of class, some students can feel more distant to the instructor and to each other, and in many cases active class participation was difficult in hybrid learning environments. Although Bulow's review (2022) focused on the challenges and opportunities of designing effective hybrid learning environments for the teacher, it follows that students participating in different environments will also need to adapt to foster effective active participation environments that encompass both local and remote learners.

Engagement in Emergency Remote Instruction During COVID-19

There is currently a thin literature on student perceptions of the efficacy of ERT strategies and formats in engaging students during COVID-19. Indeed, student perceptions about online learning do not indicate actual learning. This study considers student perceptions for the purpose of gathering information about what conditions help or hinder students' comfort with engaging in online classes toward the goal of designing improved online learning opportunities in the future. The large scale surveys of undergraduate students had some items relating to engagement, but these surveys aimed to generally understand the student experience during the transition to COVID-19 induced ERT ( Means and Neisler, 2020; Read, 2020). A few small studies have surveyed or interviewed students from a single course on their perceptions of the changes made to courses to accommodate ERT ( Senn and Wessner, 2021), the positives and negatives of ERT ( Hussein et al., 2020), or the changes in their participation patterns and the course structures and instructor strategies that increase or decrease engagement in ERT ( Perets et al., 2020). In their survey of 73 students across the United States, Wester and colleagues specifically focused on changes to students' cognitive, affective, and behavioral engagement due to COVID-19 induced ERT, but they did not inquire as to what were the key influencing factors for these changes. Walker and Koralesky (2021) and Shin and Hickey (2021) surveyed students from a single institution but from multiple courses and thus are most relevant to the current study. These studies aimed to understand the students' perceptions of their engagement and influencing factors of engagement at a single institution, but they did not assess how often these factors were implemented at that institution.

The current study investigates the engagement strategies used in a large, public, research institution, students' opinions about these course methods, and students' overall perception of learning in-person versus during ERT. This study aims to answer the following questions:

1. How has the change from in-person to online learning affected student attendance, performance expectations of students, and participation in lectures?

2. What engagement tools are being utilized in lectures and what do students think about them?

3. What influence do social interactions with peers, teachers, and administration have on student engagement?

These three questions encompass the three different dimensions of engagement, including multiple facets of each, as well as explicitly highlighting the role of technology in student engagement.

Materials and Methods

Data were collected from two main sources: a survey of undergraduates, and Course and Professor Evaluations (CAPE). The study was deemed exempt from further review by the institution's Institutional Review Board because identifying information was not collected.

Survey

The survey consisted of 50 questions, including demographic information as well as questions about both in-person and online learning (Refer to full survey in Supplementary Material.). The survey, hosted on Qualtrics, was distributed to undergraduate students using various social media channels, such as Reddit, Discord, and Facebook, in addition to being advertised in some courses. In total, the survey was answered by 237 students, of which 187 completed the survey in full, between January 26th and February 15, 2021. It was made clear to students that the data collected would be anonymous and used to assess engagement over the course of Fall 2019 to Fall 2020. The majority of the survey was administered using five-point Likert scales of agreement, frequency, and approval. The survey was divided into blocks, each of which used the same Likert scale. Quantitative analysis of the survey data was conducted using R, and visualized with the likert R package ( Bryer and Speerschneider, 2016).

A number of steps were taken to ensure that survey responses were valid. Before survey distribution, 2 cognitive interviews were conducted with undergraduate students attending the institution in order to refine the intelligibility of survey items ( Desmione and Carlson Le Floch, 2004). Forty-eight incomplete surveys were excluded. In addition, engagement tests were placed within the larger blocks of the survey in order to prevent respondents from clicking the same choice repeatedly without reading the prompts. The two students who answered at least one of these questions incorrectly were excluded.

Respondent Profile

Respondents were asked before the survey to confirm that they were undergraduate students attending the institution over the age of 18. Among the 187 students that filled out the survey in its entirety, 21.9% were in their first year, 28.3% in their second year, 34.2% in their third year, 11.8% in their fourth year, and 1.1% in their fifth year or beyond. It should be noted, therefore, that some students, especially first-years, had no experience with in-person college education at the institution, and these respondents were asked to indicate this for any questions about in-person learning. However, all students surveyed were asked before participating whether they had experience with online learning at the institution. 2.7% of respondents were first year transfers. 72.7% of overall respondents identified as female, 25.7% as male, 0.5% as non-binary, and 1.1% preferred not to disclose gender. In regards to ethnicity, 45.6% of respondents identified as Asian, 22.8% as White, 13.9% as Hispanic/Latinx, 1.7% as Middle Eastern, 1.6% as Black or African-American, and 2.2% as Other. 27.9% of respondents were first-generation college students, 7.7% of respondents were international students, and 9.9% of students were transfer students.

In the most recent report for the 2020-2021 academic year, the Institutional Research Department noted that out of 31,842 undergraduates, 49.8% of undergraduates are women and 49.4% are men ( University of California, San Diego Institutional Research, 2021). This report states that 17% of undergraduates are international students, which is a larger percentage than is represented by survey respondents ( University of California, San Diego Institutional Research, 2021). The institution reports 33% of undergraduates are transfer students, which are also underrepresented in the survey respondents ( University of California San Diego [UCSD], 2021b). The ethnicity profile of the survey respondents is similar to the undergraduate student demographic at this institution. According to the institutional research report, among undergraduates, 37.1% are Asian American, 19% are White, 20.8% are Chicano/Latino, 3% are African American, 0.4% are American Indian, and 2.5% are missing data on ethnicity ( University of California, San Diego Institutional Research, 2021).

Course and Professor Evaluation Reviews

Data were also collected from the institution's CAPE reviews, a university-administered survey offered prior to finals week every quarter, in which undergraduate students are asked to rate various aspects of their experience with their undergraduate courses and professors ( Courses not CAPEd for Winter 22, 2022). CAPE reviews are anonymous, but are sometimes incentivized by professors to increase participation.

Although it was not designed with Bond and Bedenlier's student engagement framework in mind, the questions on the CAPE survey still address the fundamental influences on engagement established by the framework. The CAPE survey asks students how many hours a week they spend studying outside of class, the grade they expect to receive, and whether they recommend the course overall. The survey then asks questions about the professor, such as whether they explain material well, show concern for student learning, and whether the student recommends the professor overall.

In this study, we chose to look only at data from Fall 2019, a quarter where education was in-person, and Fall 2020, when courses were online. In Fall 2019, there were 65,985 total CAPE reviews submitted, out of a total of 114,258 course enrollments in classes where CAPE was made available, for a total response rate of 57.8% ( University of California San Diego [UCSD], 2021a). The mean response rate within a class was 53.1% with a standard deviation of 20.7%. In Fall 2020, there were 65,845 CAPE responses out of a total of 118,316 possible enrollments, for a total response rate of 55.7%. The mean response rate within a class was 50.7%, with a standard deviation of 19.6%.

In order to adjust for the different course offerings between quarters, and for the different professors who might teach the same course, we selected only CAPE reviews for courses that were offered in both Fall 2019 and Fall 2020 with the same professors. This dataset contained 31,360 unique reviews (16,147 from Fall 2019 and 15,213 from Fall 2020), covering 587 class sections in Fall 2019 and 630 in Fall 2020. Since no data about the students were provided with the set, however, we do not know how many students these 31,360 reviews represent. This pairing strategy offers many interesting opportunities to compare the changes and consistencies of student reviews between both quarters in question. To keep this study focused on the three research questions and in observation of time and space limitations, analysis was only performed on the pairwise level of the general CAPE survey questions and not broken down to further granularity.

The CAPE survey was created by the designers of CAPE, not the researchers of this paper. The questions on the CAPE survey are general and only provide a partial picture of the status of student engagement in Fall 2019 and Fall 2020. The small scale survey created by this research team attempts to clarify and make meaning of the results from the CAPE data.

Data Analysis

Survey Data

Survey data was collected and exported from Qualtrics as a. csv file, then manually trimmed to include only relevant survey responses from participants who completed the survey. Data analysis was done in R using the RStudio interface, with visualizations done using the likert and ggplot2 R packages ( Bryer and Speerschneider, 2016; Wickham, 2016; R Core Team, 2020; RStudio Team, 2020). Statistical tests were performed on lecture data, using paired t-tests, and Mann-Whitney U tests of the responses; for example, when comparing attendance of in-person lectures in Fall 2019 and live online lectures on Zoom in Fall 2020.

Course and Professor Evaluation Data

As previously mentioned, analysis of CAPE reviews was restricted to courses that were offered in both Fall 2019 and 2020 with the same professor, with Fall 2019 courses being in-person and Fall 2020 courses being online. This was done since the variation of interest is the change from in-person to online education, and restricting analysis to these courses allowed the pairing of specific courses for statistical tests, as well as the adjustment for any differences in course offerings or professor choices between the two quarters. In order to compare ratings for a specific item, first, negative items were recoded if necessary. The majority of questions were on a 5-point Likert scale, though some, such as expected grade, needed conversion from categorical (A-F scale) to numerical (usually 0-4). Then, the two-sample Mann-Whitney U test was conducted on the numerical survey answers, comparing the results from Fall 2019 to those from Fall 2020. Results were then visualized using the R package ggplot2 ( Wickham, 2016), as well as the likert package ( Bryer and Speerschneider, 2016).

Results

In this study, we aimed to take a broad look at the state of online learning at UCSD as compared to in-person learning before the COVID-19 pandemic. This assessment was split into three general categories: changes in lecture engagement and student performance, tools that professors and administrators have implemented in the face of online learning, and changes in patterns of students' interactions with their peers and with instructors. In general, while we found that students' ratings of their professors and course staff remained positive, there were significant decreases in lecture engagement, attendance, and perceived ability to keep up with coursework, even as expected grades rose. In addition, student-student interactions fell for the vast majority of students, which students felt hurt their learning experience.

Course and Professor Evaluation Results

How Has the Change From in-Person to Online Learning Affected Student Attendance, Performance Expectations, and Participation in Lectures?

Lecture Attendance

In the CAPE survey, students reported their answers to a series of questions relating to lecture attendance and engagement. Table 1 reports the results of the Mann-Whitney U test for each question, in which the results from Fall 2019 were compared to the results from Fall 2020. Statistically significant differences were found between students' responses to the question "How often do you attend this course?" (rated on a 1-3 scale of Very Rarely, Some of the Time, and Most of the Time), although students were still most likely to report that they attended the class most of the time. Statistically significant decreases were also found for students' agreement to the questions "Instructor is well-prepared for classes," and "Instructor starts and finishes classes on time." It should be noted that "attendance" was not clarified as "synchronous" or "asynchronous" attendance to survey respondents.

Expected Grades

Within the CAPE survey, students are asked, "What grade do you expect in this class?" The given options are A, B, C, D, F, Pass, and No Pass. The proportion of CAPE responses in which students reported taking the course Pass/No Pass stayed relatively constant from Fall 2019 to Fall 2020, going from 6.5% in Fall 2019 to 6.4% in Fall 2020. As can be seen in Figure 1, participants were more likely to expect A's in Fall 2020; in Fall 2019, the median expected grade was an A in 56.8% of classes, while in Fall 2020, this figure was 68.0%. We used a Mann-Whitney U test to test our hypothesis that there would be a difference between Fall 2019 and Fall 2020 expected grades because of students' and instructors' unfamiliarity with the online modality. When looking solely at classes in which students expected to receive a letter grade, after recoding letter grades to GPA equivalents, a significant difference was found between expected grades in Fall 2019 and 2020, with a mean of 3.443 in FA19 and 3.538 in FA20 ( U = 92286720, p < 0.001).

What Engagement Tools Are Being Utilized by Professors and What do Students Think About Them?

Assignments and Learning

As part of the CAPE survey, respondents were asked to rate their agreement on a 5-point Likert scale to questions about their assignments and learning experience in the class. Results are displayed in Table 2. Statistically significant increases in student agreement, as indicated by the two-sample Mann-Whitney U test, were reported in the questions "Assignments promote learning," "The course material is intellectually stimulating," and "I learned a great deal from this course."

What Influence do Social Interactions With Peers, Teachers, and Administration Have on Student Engagement?

Professor Efficacy and Accessibility

As part of the CAPE survey, students also rated their professors in various aspects, as can be seen in Table 3. The only significant result observed between Fall 2019 and Fall 2020 was a slight increase in student agreement with the statement "Instructor is accessible outside of class."

Survey Results

How Has the Change From in-Person to Online Learning Affected Student Attendance, Performance Expectations, and Participation in Lectures?

General Satisfaction

Respondents were asked to indicate their agreement on a 5-point Likert scale (Strongly Disagree, Disagree, Neither Agree nor Disagree, Agree, and Strongly Agree) to the statement, "In general, I am satisfied with my online learning experience at [institution]." 36% of respondents agreed with the statement, 28% neither agreed nor disagreed, and 36% disagreed.

Perceptions of Academic Performance

Students were asked to rate their agreement on a 5-point Likert scale of agreement to a series of broad questions about their online learning experience, some of which pertained to academic performance. When assessing the statement "My current online courses are more difficult than my past in-person courses," 42% chose Strongly Agree or Agree, 32% chose Neither Agree nor Disagree, and 26% chose Disagree or Strongly Disagree. Respondents were also split on the statement "My academic performance has improved with online education," which 28% agreed/strongly agreed with, 34% disagreed/strongly disagreed with, and 38% chose neither.

For the statement "I feel more able to manage my time effectively with online education than with in-person education," only 34% agreed/strongly agreed with the statement while 45% disagreed/strongly disagreed and 21% chose neither. For the statement, "I feel that it is easier to deal with the pace of my course load with online education than with in-person education," 30% of respondents agreed/strongly agreed, 54% disagreed/strongly disagreed, and 16% neither agreed nor disagreed.

Lecture Attendance by Class Type

Since the CAPE survey question regarding attendance did not specify asynchronous or synchronous attendance, students were asked on the survey created by the authors of this paper how often they attended and skipped certain types of lectures. In response to the question "During your last quarter of in-person classes, how often did you skip live, in-person lectures?," 11% reported doing so often or always, 14% did so sometimes, and the remaining 74% did so rarely or never. The terms "Sometimes" and "Rarely" were not clarified to the respondents. This is the same scale and language used on the CAPE survey, however, which was a benefit to synthesizing and comparing this data with CAPEs. Meanwhile, for online classes, 35% reported skipping their live classes often or always, 23% did so sometimes, and 43% did so rarely or never.

Respondents were also asked about their recorded lectures, both in-person and online; while some courses at the institution are recorded and released in either audio or video form for students, most online synchronous lectures are recorded. When asked how often they watched recorded lectures instead of live lectures in-person, 12% of respondents said they did so often or always, 12% reported doing so sometimes, and 76% did so rarely or never. For online classes where recorded versions of live lectures were available, 47% of students reported watching the recorded version often or always, 21% did so sometimes, and 33% did so rarely or never.

Meanwhile, there were also some lectures during online learning that were offered only online (asynchronous), as opposed to being recorded versions of lectures that were delivered to students live over Zoom.

Students were asked questions about their lecture attendance for in-person learning pre-COVID and for online learning during the pandemic. On a 5 point Likert scale from Never to Always, 11% of students said they skipped "live, in-person lectures" in their courses pre-COVID Often or Always. On the same scale, 35% of respondents said they skipped live online lectures Often or Always. To assess the significance of these reports, we conducted a one-sided Mann-Whitney U test with the null hypothesis that the median frequency of students skipping live online lectures is greater than the median frequency of skipping live in-person lectures. Previous research suggesting that lecture attendance decreased after the COVID-19 transition motivated our alternative hypothesis that students would skip live online lectures more often ( Perets et al., 2020). The result was significant, meaning that this evidence suggests that students skip online lectures (Mdn = 3 "Sometimes") more often than live in-person lectures (Mdn = 2 "Rarely"), U = 23328, p < 0.001. The results were also significant when a one-sided 2 sample t-test was performed to test if students were skipping online lectures ( M = 2.84, SD = 1.13) more often than they skipped in-person lectures ( M = 1.97, SD = 1.06), t(358.53) = 7.55, p < 0.001.

In order to clarify why students might be skipping lectures, we asked students how often they were using the recorded lecture options during in-person and online learning. 12% of respondents reported that they watched the recorded lecture "Often" or "Always" instead of attending the live lecture in-person while 47% of respondents said that they watched the recorded version of lecture, if it was offered, "Often" or "Always" rather than the live version during remote learning. When a one-sided Mann-Whitney U test was performed comparing the medians of students that utilized the recorded option during in-person classes (Mdn = 2 "Rarely") and during online classes (Mdn = 3 "Sometimes"), the results were significant, suggesting that more students watch a recorded lecture version when it is offered during online classes, U = 6410, p < 0.001. The results are also significant with a t-test comparing the means of students that watched the recorded format during in-person classes ( M = 1.95, SD = 1.08) and during online classes ( M = 3.23, SD = 1.23), t(330.84) = -10.13, p < 0.001.

Students were asked how often they used course materials, such as a textbook or instructor provided notes and slideshows, rather than attending a live or recorded lecture to learn the necessary material. 10% of students said that they used course materials "Often" or "Always" during in-person learning while 19% of students said they used course materials "Often" or "Always" during online learning. The results were significant in a one-sided Mann-Whitney U test for the null hypothesis that the medians are equivalent for students using materials during in-person learning (Mdn = 1 "Never") and during online learning (Mdn = 2 "Rarely"), U = 12644, p < 0.001. In other words, the evidence suggests that students use course materials instead of attending lectures more often when classes are online than when classes are in-person. A one-sided t-test also indicates that students during online learning ( M = 2.30, SD = 1.16) utilize provided materials instead of watching lecture to learn course material more often than students during in-person learning ( M = 1.76, SD = 1.03), t(364.55) = -4.72, p < 0.001.

Discussions are supplementary and sometimes mandatory classes to the lecture conducted by a teaching assistant. Students reported that during the last quarter of online classes the discussion sections tended to include synchronous live discussion instead of pre-recorded content (see Table 4).

Reported Attendance and Engagement in Lecture

Students were asked to rate their agreement on the same 5-point Likert scale to a series of questions about their in-lecture attendance and engagement. When presented with the statement "I feel more comfortable asking questions in online classes than in in-person ones," 56% of students agreed, 22% neither agreed nor disagreed, and 22% disagreed. Here, "agreed" includes strongly agree and disagree includes "strongly disagreed." This was similar to the result for "I feel more comfortable answering questions in online classes than in in-person ones," to which 56% agreed, 24% neither agreed nor disagreed, and 20% disagreed.

When students who had taken both in-person and online courses were directly asked about overall attendance of live lectures, with the statement "I attend more live lectures now that they are online than I did when lectures were in-person," 12% agreed, 19% neither agreed nor disagreed, and 69% disagreed (with 32.5% selecting "Strongly disagree").

Issues With Online Learning

Respondents were asked to indicate on a 5-point Likert frequency scale (Never, Rarely, Sometimes, Often, and Always) how often a series of possible issues affected their online learning. These are reported in Figure 2. The most common technical issue was unreliable WiFi. 20% of students say unreliable WiFi happens "Often" or "Always," 35% say this issue happens to them "Sometimes," and 45% of students say unreliable WiFi affects their online learning "Never" or "Rarely." The next common technological problem students face is unreliable devices. A poor physical environment affected students' online learning for 32% of the respondents "Often" or "Always." Issues with platforms, such as Gradescope, Canvas, and Zoom, were present but reported less often.

Figure 2. Prevalence of issues in online education among student survey respondents ( n = 187).

What Engagement Tools Are Being Utilized by Professors and What do Students Think About Them?

Course Structure

For a given possible intervention in course structure, students were asked how often their professors implemented the changes and to rate their opinion of the learning strategy. The examined changes were weekly quizzes, replacing exams with projects or other assignments, interactive polls or questions during lectures, breakout rooms within lectures, open-book or open-note exams, and optional or no-fault final exams - exams that will not count toward a student's overall grade if their exam score does not help their grade.

Respondents' reported frequencies of these interventions are displayed in Figure 3, and their ratings of them are displayed in Figure 4. In addition to being the most common intervention, open book exams were also the most popular intervention among students, with 89% of respondents reporting that they had a Good or Excellent opinion. Similarly popular were in-lecture polls, optional finals, and replacing exams with assignments, while breakout sessions had a slightly negative favorability.

Academic Tools and Resources

In the survey, students were asked to rate their agreement with the statement, "Online learning has made me more likely to use academic resources such as office hours, tutoring, or voluntary discussion sessions." 42% of students agreed (includes "strongly agreed"), 23% neither agreed nor disagreed, and 35% disagreed (includes "strongly disagreed"). However, for the statement, "Difficulties accessing office hours or other academic resources have negatively interfered with my academic performance during online education," 26% of students agreed/strongly agreed, 24% neither agreed nor disagreed, and 49% disagreed/strongly disagreed.

Respondents were asked to rate their opinion of various academic resources on a 5-point scale (Terrible, Poor, Average, Good, and Excellent) for both in-person and online classes ( Figures 5, 6). The most notable change in rating was for the messaging platform Discord, which 67% of respondents saw as a Good or Excellent academic resource during online education, compared to 34% in in-person education. The learning management system Canvas also saw an increase in favorability, while favorability decreased for course discussions.

Cameras

Respondents were asked to rate the frequency at which they and their professors turned their cameras on during lectures. 64% of students reported keeping their cameras on never or rarely, 29% reported keeping cameras on sometimes, and 6% of students reported keeping their cameras on often or always. Meanwhile, for professors, 58% of students reported that all of their professors kept their cameras on, 28% said most kept their cameras on, 9% said about half did so, and the remaining 5% said that some or none of their professors kept cameras on.

What Influence do Social Interactions With Peers, Teachers, and Administration Have on Student Engagement?

Personal Interaction

A lack of social interaction was among the largest complaints of students about online learning. 88% of respondents at least somewhat agreed with the statement "I feel less socially connected to my peers during online education than with in-person education." When students were asked how often certain issues negatively impacted their online learning experience, 64% of respondents indicated that a lack of interaction with peers often or always impacted their learning experience, and 44% reported the same about a lack of instructor interaction.

When we asked students how they stay connected to their peers, 78.6% said that they stay connected to peers through student-run course forums, such as Discord, a messaging app that is designed to build communities of a common interest. 72.7% said they use personal communication, i.e., texting, with peers. 48.1% of students said they use faculty-run course forums, such as Piazza or Canvas. 45.5% of students surveyed keep in touch with peers through institution clubs and organizations. 29.4% of students selected that they use student-made study groups and 19.8% stay connected through their campus job.

Ratings of University Faculty and Staff

Students were asked to rate their opinion of various faculty and staff, by answering survey statements of the form "____ have been sufficiently accommodating of my academic needs and circumstances during online learning." For instructors, 72% agreed/strongly agreed with this statement and 11% disagreed/strongly disagreed; for teaching assistants and course tutors, 81% agreed/strongly agreed, and only 2% disagreed/strongly disagreed. Meanwhile, for university administration, 39% of students agreed/strongly agreed, 34% neither agreed nor disagreed, and 26% disagreed/strongly disagreed.

Discussion

Based on both the prior literature and this study, students seemed to struggle with engagement before the pandemic during in-person lectures, and it appears from the survey findings that students are struggling even more with engagement in online courses. A U.S. study investigating the teaching and learning experiences of instructors and students during the COVID-19 pandemic also found that when learning transitioned online, students' main issue was engagement whereas prior to the pandemic the main issue for students was content ( Perets et al., 2020). The lack of peer connection and technological issues seem to be significant problems for students during online learning and could contribute to students' issues with engagement. The problems with attention during an online lecture might be attributed to the lack of social accountability that an in-person lecture promotes to put away distractions like cell phones and taking active notes. Additionally, CAPE data shows that students rate their professors' efforts and course design highly and similarly before and during Fall 2019 and Fall 2020. Although every course and professor has different requirements, creating collaborative opportunities and incorporating interactive features into lectures could be beneficial to student engagement.

How Has the Change From in-Person to Online Learning Affected Student Attendance, Performance Expectations, and Participation in Lectures?

For live lectures, the increase in students reporting skipping live online lectures more often may be due to the increase in availability and ease of recorded options with online lectures. A similar study to this research found that when the university transitioned to Pass/No Pass grading rather than letter grading during ERT, students attended synchronous lectures less ( Perets et al., 2020). During the pandemic, the institution's deadline to change to P/NP grading was extended and more academic departments allowed Pass/No Pass classes to fulfill course requirements. In our study, we did not detect an increase in students who took advantage of the P/NP grading, but it is possible that students skipped more synchronous lectures knowing that they could use the Pass option as a safety net if they did not dedicate the typical amount of lecture time to learn the material. The results emphasize the vital role of the cognitive dimension in engagement.

It is clear that more students are taking advantage of recorded options with online learning. A survey of Harvard medical students indicates a preference for the recorded option because of the ability to increase the speed of the lecture video and prevent fatigue ( Cardall et al., 2008). Consistent with previous research, our results suggest that students may seek more value and time management options from course material when classes are fully online ( Perets et al., 2020). Recorded lectures allow freedom for students to learn at a time that works best for them ( Rae and McCarthy, 2017). For discussions, students reported that they had more discussions that were live rather than recorded. Research indicates that successful online learning requires strong instructor support ( Dixson, 2010; Martin and Bolliger, 2018). The smaller class setting of a discussion, even virtual, may promote better engagement through interaction among the students, content, and the discussion leader.

Based on CAPE results, which are conducted the week before final exams, students expected higher grades during the online learning period. Although expected grades rose, students concur with previous surveys that the workload was overwhelming and was not adequately adjusted to reflect the circumstances of ERT ( Hussein et al., 2020; Shin and Hickey, 2020). While there are many factors that could account for this, including the fact that expected grades reported on CAPE do not reflect a student's actual grade, one possible explanatory factor is the use of more lenient grading standards and course practices during the pandemic. In addition to relaxed Pass/No Pass standards, courses were more likely to adopt practices like open-book tests or no-fault finals, providing students with assessments that emphasized a demonstration of deeper conceptual understanding rather than memorization. It is important to note that students' perceptions of their learning does not indicate that students are actually learning or performing better academically. This goes for the CAPE question, "I learned a great deal from this course," the CAPE question about expected grades, and the small scale survey reports about academic performance. We took interest in these questions because they offer insight into the level of difficulty students perceived during ERT due to the shift in engagement demands from remote learning. More research should be done with students' academic performance data before and after ERT to clarify whether there was a change in students' learning.

Students' preference for using a virtual platform during lecture to ask, answer, and respond to questions was surprising. This extends previous evidence from Vu and Fadde (2013), who found that, in a graduate design course at a Midwestern public university with both in-person and online students in the same lecture, students learning online were more likely to ask questions through a chat than students attending in-person lectures. In addition, during the COVID-19 pandemic, Castelli and Sarvary (2021) report that Zoom chat facilitates discussions for students, especially for those who may not have spoken in in-person classes.

When students were surveyed on the issues they faced with online learning, the most common issues had to do with engagement in lectures, interaction with instructors and peers, and having a poor physical work environment, while technical issues or issues with learning platforms were less common. The distinction between frequency and impact is key, since issues such as bad WiFi connection can be debilitating to online learning even if uncommon, and issues with technology and physical environment also correlate with equity concerns. Other surveys have found that students and faculty from equity-seeking groups faced more hardships during online learning because of increased home responsibilities and problems with internet access ( Chan et al., 2020; Shin and Hickey, 2020). Promoting student engagement in class involves more than well-planned teaching strategies. Instructors and universities need to look at the resources and accessibility of their class to reduce the digital divide.

According to the CAPE data from Table 2, instructors received consistent reviews before and after the ERT switch, indicating that they maintained their effectiveness in teaching. The ratings for two CAPE prompts "Instructor is well prepared for class" and "Instructor starts and finishes class on time" had statistically significant decreases from Fall 2019 to Fall 2020. This decrease could be attributed to increased technological preparation needed for online courses and the variety of offerings for lecture modalities. For example, some instructors chose to offer a synchronous lecture at a different time than the original scheduled course time, and then provide office hours during their scheduled lecture time to discuss and review the lectures. Regardless of the statistically significant changes, the means for these two statements are high and similar to Fall 2019.

What Engagement Tools Are Being Utilized in Lectures and What do Students Think About Them?

Based on the results, a majority of students report that their professors are using weekly quizzes, breakout rooms, and polls at least sometimes in their classes to engage students. Students had highly positive ratings of in-course polling, were mostly neutral or positive about weekly quizzes (as a replacement for midterm or final exams), but were slightly negative about breakout rooms. Venton and Pompano (2021) report positive qualitative student feedback from students in chemistry classes at the University of Virginia, with some students finding it easier to speak up and make connections with peers than in an entire class; Fitzgibbons et al. (2021), meanwhile, found in a sample of 15 students at the University of Rochester that students preferred working as a full class instead of in breakout rooms, though students did report making more peer connections in breakout rooms. Breakouts have potential to strengthen student-student and student-instructor relationships, but further research is needed to clarify their effectiveness.

Changes were also made to course structure, with almost all (94%) of students reporting that open-book exams were used at least sometimes. Open-book exams were also the most popular intervention overall, although the reason for their widespread adoption (academic integrity and fairness concerns) is likely different from the reasons that students like them (less focus on memorization). Open-book tests, however, present complications. Bailey et al. (2020) notes that while students still needed a good level of understanding to succeed on open-book exams, these exams were best suited to higher-order subjects without a unique, searchable answer.

Changes were detected in the responses to the CAPE statements, "Assignments promote learning," "The course material is intellectually stimulating," and "I learned a great deal from this course," noted in Table 3. Although there were statistically significant changes detected by the Mann-Whitney U Test, the means between Fall 2019 and Fall 2020 are still similar and positive. The results from this table indicate that students felt that there was not a decrease in learning and interest in their material. This might be due to instructors changing the design of assessments and assignments to accommodate for academic integrity and modality circumstances in the online learning format. The consistently positive CAPE ratings are also likely due to the fact that students are aware that CAPEs are an important factor for the departments' hiring and retention decisions for faculty, and subsequently important for their instructors' careers. Students may have also recognized that most of the difficulties in the switch to online learning were not the instructors' fault. Students' sympathy for the challenges that instructors faced may be contributing to the slightly more positive reviews during Fall 2020.

What Influence do Social Interactions With Peers, Teachers, and Administration Have on Student Engagement?

One of the most common experiences reported by students was a decrease in interaction with peers, with a strong majority of students saying that a lack of peer interaction hurt their learning experience. A study from Central Michigan University shows that peer interaction through in class activities supports optimal active learning ( Linton et al., 2014). Without face-to-face learning and asynchronous classes during COVID, instructors were not able to conduct the same collaborative activities. When asked how students interacted with their peers, the most common responses were student-run course forums or texting. This seems to support the findings of Wong (2020) which indicated that during ERT, students largely halted their use of synchronous forms of communication and opted instead for asynchronous ones, like instant messaging, with possible impacts on students' social development. Students also reported a decrease in interaction with their instructors with a plurality saying that a lack of access to their instructors affected their academic experience. At the same time, ratings of professors' ability to accommodate for the issues students faced during online education were high, as were students' ratings of online office hours. It seems that students sympathized with instructors' difficulties in the ERT transition but were aware that the lack of instructor presence impacted their learning experience nonetheless.

Limitations

There are some limitations in this study that should be considered before generalizing the results more widely. The survey was conducted at just a single university, UCSD: a large, highly-ranked, public research institution in the United States with its own unique approach to the COVID-19 pandemic. These results would likely differ significantly for online education at other universities. In addition, though care was taken to distribute the survey in channels used by all students, the voluntary response of students chosen from these channels does not constitute a simple random sample of undergraduates attending this institution. For example, our survey over-represents female students, who constituted 72.7% of the survey sample. The channels chosen could also bias certain results; for example, it is possible that students who answer online surveys released on the institution's social media channels are less likely to have technical or Internet difficulties. Results from the small survey might be skewed slightly because respondents had to recall a year prior to their experiences in Fall 2019, whereas they might have had a more accurate memory of their Fall 2020 experience. CAPEs are completed at the end of the quarter when their recollection of their experiences is fresh, so those reviews are likely less susceptible to this unconscious bias.

The issues with sampling are somewhat mitigated in the CAPE data, but these responses are not themselves without issue. CAPE reviews are still a voluntary survey, and therefore are not a random sample of undergraduates. In addition, some instructors use extra credit to incentivize students to participate in CAPEs if the class meets a threshold percentage of responses, which might skew the population of respondents. CAPE responses tend to be relatively generous and positive, with students rating instructors and educational quality much higher in CAPE reviews than in our survey. This is possibly because the CAPE forms make it easy for students to report the most positive ratings on every item without considering them individually. Additionally, students are aware that CAPEs have an impact on the department's decisions to rehire instructors.

Teaching Implications

Online learning presented multiple challenges for instructors and students, illuminating areas to improve in higher education that were not recognized before the COVID-19 pandemic. A majority of students expressed their comfort in engaging with the Zoom chat and polling. Students might feel this way since they can ask and answer questions using the chat feature without disrupting the focus in class. Therefore, in both further online learning and in-person classes, instructors might be able to stimulate interaction by lowering the social barriers to asking and answering questions. Applications such as Backchannel Chat, Yo Teach!, and NowComment offer more features than Zoom or Google Meet to prevent fatigue and increase retention in-person or online ( LearnWeaver, 2014; Hong Kong Polytechnic University, 2018; Paul Allison, 2018).

At the same time, increased interactivity in lectures, especially if required, is not necessarily a panacea for engagement issues. For example, some professors might require students to turn on their cameras, increasing accountability and giving an incentive to visibly focus as if in an in-person classroom. However, Castelli and Sarvary (2021) found, as we did, that the majority of students in an introductory collegiate biology course kept their cameras off; students cited concerns about their appearance, other people being seen behind them, and weak internet connections as the most common reasons for not keeping cameras on. Not only are these understandable concerns, but they correlate with identity as well: Castelli and Sarvary found that both underrepresented minorities and women were more likely to indicate that they worried about cameras showing others their surroundings and the people behind them.

Conclusion

Prior to the COVID-19 pandemic, online learning was a choice. Our research demonstrates that online learning has a long way to go before it can be used in an equitable manner that creates an engaging environment for all students, but that instructors adapted well to ERT to ensure courses promoted the same level of learning. The sudden nature of remote learning during the COVID pandemic did not allow for instructors or institutions to research and promote the most engaging online learning resources. Students have widely varying opinions and experiences with their higher education online learning experience during the pandemic. Our data analysis shows that distance learning during the pandemic had a toll on attendance during live lecture and peer-instructor connection. The difference in expected grades from Fall 2019 to Fall 2020 indicates that students felt differently about their ability to succeed in their online classes. In addition, students had trouble managing work loads during online learning. We gathered that instructors could be using engagement strategies more often to match students' enthusiasm for those strategies, such as chat features and polls. Despite the challenges of online learning highlighted, this research also presents evidence that online learning can be engaging for students with the right tools. Student reviews indicated similarity before and after the switch to online learning, including indicating that course assignments promoted learning and the material was intellectually stimulating. These results propose that the courses and professors, despite the modality switch and changes to teaching and assessment strategies, maintained the level of learning that students felt they were getting out of their course.

Data Availability Statement

The data supporting the conclusions of this article contains potentially identifiable information. The authors can remove this identifying information prior to sharing the data.

Author Contributions

BH contributed to this project through formal analysis, investigation, and writing. PN contributed to the project through formal analysis, investigation, visualization, and writing. LC contributed to conceptualization, resources, supervision, writing, review, and editing. SH-L contributed methodology, supervision, writing, review, and editing. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We would like to acknowledge the Qualcomm Institute Learning Academy for supporting this project.

References

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Engagement in Online Learning: Student Attitudes and Behavior During COVID-19

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The COVID-19 pandemic resulted in nearly all universities switching courses to online formats. We surveyed the online learning experience of undergraduate students (n = 187) at a large, public research institution in course structure, interpersonal interaction, and academic resources. Data was also collected from course evaluations.

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Educational Technology Research and Development 69, 113-116 (2021) Cite this article

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Abstract

This paper was developed in response to the article titled, "A design framework for enhancing engagement in student-centered learning: Own it, learn it, share it," by Eunbae Lee and Michael J. Hannafin (2016). Their work is examined through a research perspective. The authors' discovery of the lack of a comprehensive framework to guide the design, development, and implementation of student-centered learning prompted them to develop the model. The result advances a three-stage model known as, Own It, Learn It, and Share It. The impact of the model can be understood through the simplicity of the OLSit model by how it affords educators a clear pathway for transforming instructional planning. Given the many uncertainties associated with the "shift to digital" as a result of COVID-19, the OLSit model is very applicable for action research by teachers, student teachers, and teacher educators. A critique of the model is provided along with suggested applications to extend the OLSit research base.

Lee and Hannafin's ( 2016) discovery of the lack of a comprehensive framework to guide the design, development, and implementation of student-centered learning prompted them to develop the OLSit model. During this COVID-19 era where schools have been required to shift from traditional face-to-face instruction to remote instruction, this work is worthy of re-examination.

The OLSit model was built from a literature synthesis of research associated with student engagement, autonomy, scaffolding, motivation, and meta-cognition. The result advances a three-stage OLSit model known as, Own It, Learn It, and Share It.

In the Own It phase, students define their purpose and create a learning plan with the assistance of the teacher. This feature distinguishes the model from pure discovery learning where the student is left floundering to one where the teacher provides guidance. Guidance may be in the form of suggested resources, insights about how to set boundaries, or ideas about how to explore content more deeply. In the Learn It phase, the student engages in learning activities operationalized through a learning plan. Finally, in the Share It phase, the student identifies one or more channels for sharing what they have learned with an authentic audience.

Impact/value

In the typical teacher-directed bell-to-bell managed classroom, few K-12 students have been afforded the opportunity to engage in learning with a style, pace, and depth of their own choosing. As a result, most students (and parents!) were wholly unprepared for the level of independent learning required during pandemic remote instruction (Dorn, Hancock, Sarakatsannis, & Viruleg, 2020). Since many teacher's first instinct was to try to replicate face-to-face direct instruction methods using distance learning technologies, this was problematic for many reasons and teachers became justifiably concerned about the level of student engagement during remote instruction (Simmons, 2020). Parents were concerned about the amount of time they were required to sit with their child as a teacher proxy to assist them in completing their assignments (Perelman, 2020).

To-date, few teachers have considered flipping the paradigm. That is, instead of attempting to implement direction instruction at a distance, project-based learning offers a powerful alternative to promoting student engagement. The research literature is very clear, self-regulation and self-directed learning strategies need to be taught, and practiced, over time (Dignath, Buettner, & Langfeldt, 2008; Pandey et al., 2018). Project-based learning has the potential to enhance student achievement during a period when COVID-19 learning loss is an increasingly concern.

The simplicity of the OLSit model affords educators a clear pathway for transforming instructional planning. The shift in responsibility from teacher-focused to student-centered instruction can be readily accomplished via a restructuring of learning assignments. Conversations between the teacher and student, whether synchronous or asynchronous, provide the necessary guidance for the student to create a personalized learning plan. The student's management of his/her learning plan offers a necessary form of accountability. Finally, the model's emphasis on Share It, offers a public, and authentic demonstration of the learning outcomes. Naturally, teachers, students, and parents should not expect immediate impact after trying the strategy a single time. Over time, as the strategy is re-used and stretched in new ways, the process should become intuitive, enjoyable, and one that produces positive learning outcomes. Whereas teachers may initially be concerned about the amount of time project-based learning takes, the research indicates moderate effect sizes in learning outcomes associated engagement, project-based learning, self-regulation, and feedback (Hattie, 2009, 2012; Hattie & Yates, 2014; Lee & Hannafin, 2016).

Application

Given the many uncertainties associated with the "shift to digital" as a result of COVID-19, the OLSit model is very applicable for research by teachers, student teachers, and teacher educators. That is, what type of local knowledge can be generated by posing questions and systematically collecting data to understanding what works, for whom, and under what conditions? These types of research questions are ideal for action research studies.

Amid the chaos of the pandemic, there is a unique opportunity to gain new research insights about methods of deepening student engagement and improving educational outcomes. The artifacts generated through the OLSit model will contribute to the collection and analysis of evidence without significant additional effort. However, given the premium on every professional's time, action research may be best supported through school-university collaboration to share the workload and triangulate insights about what is being observed.

Limitations and constraints

Given the recency of the manuscript's publication, the literature has yet to see replications of the OLSit model. Naturally, there is a need for more exemplars of how the model can be used at different grade levels with diverse students. Furthermore, there is an urgent need to determine how to support students that have underdeveloped self-regulation skills (i.e., young children, some students with disabilities) or lack access to sufficient technology or learning resources in the home. It is unclear what assumptions can be made about adult availability to support the OLSit process in the home during the pandemic and how the presence/absence of adult support impacts the learning outcomes.

Future directions

Resources like the Learning Activities Types ( https://activitytypes.wm.edu/) collection at the College of William and Mary offer helpful planning structures for educators seeking to align learning activities with technology tools. However, at the present time, little is known about the learning activities types being used by educators as remote instruction is implemented. Therefore, researchers may wish to explore that specific nature of the personalized learning plans created as part of OLSit in order to gain new insights about how various learning activities foster, or negatively impact, student-centered learning.

References

Dorn, E., Hancock, B., Sarakatsannis, J., & Viruleg, E. (2020). COVID-19 and student learning in the United States: The hurt could last a lifetime. New York: McKinsey & Company Retrieved from https://www.mckinsey.com/industries/public-and-social-sector/our-insights/covid-19-and-student-learning-in-the-united-states-the-hurt-could-last-a-lifetime.

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Hattie, J. A. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. New York: Routledge.

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Perelman, D. (2020, July 2). In the Covid-19 economy, you can have a kid or a job. You can't have both. New York Times. Retrieved from https://www.nytimes.com/2020/07/02/business/covid-economy-parents-kids-career-homeschooling.html.

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Edyburn, D. Transforming student engagement in COVID-19 remote instruction: a research perspective. Education Tech Research Dev 69, 113-116 (2021). https://doi.org/10.1007/s11423-020-09919-6
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Transforming student engagement in COVID-19 remote instruction: a research perspective - Educational technology research and development

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This paper was developed in response to the article titled, "A design framework for enhancing engagement in student-centered learning: Own it, learn it, share it," by Eunbae Lee and Michael J. Hannafin (2016). Their work is examined through a research perspective.

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Department of Educational Psychology, Institute of Psychology, Goethe University, Frankfurt, Germany

The sudden impact of the COVID-19 pandemic challenged universities to provide students with online teaching and learning settings that were both immediately applicable and supportive of quality learning. This resulted in a broad variety of synchronous and asynchronous online settings of teaching and learning. While some courses balanced both kinds, others offered either predominantly synchronous or asynchronous teaching and learning. In a survey study with students ( N=3,056) and teachers ( N=396) from a large German university, we explored whether a predominance of synchronous or asynchronous teaching and learning settings in higher education was associated with certain student experiences and outcomes. Additionally, we examined how well these two types of teaching and learning settings support students' basic psychological needs for autonomy, competence, and relatedness proposed by self-determination theory (SDT). Data were collected after the first online semester due to the COVID-19 pandemic. The results imply that from the students' perspective, the teaching methods involved in the two settings of teaching and learning differ with regard to their potential to support social interaction and to support basic psychological needs as proposed by SDT. Students who studied mostly in synchronous settings reported more peer-centered activities such as feedback in comparison to students in mostly asynchronous settings. In contrast, teachers perceived fewer differences between teaching methods in synchronous and asynchronous settings, especially regarding feedback activities. Further, students in mostly synchronous settings reported greater support of their basic psychological needs for competence support and relatedness as well as a greater overall satisfaction with the online term compared to students in mostly asynchronous settings. Across all students, greater fulfillment of psychological needs and higher technology acceptance coincided with outcomes that are more favorable. Implications for the post-pandemic classroom are drawn.

Introduction

The sudden need to adapt to online teaching and learning due to the COVID-19 pandemic challenged the digital readiness of teachers and students all over the world ( Bao, 2020; Crawford et al., 2020; Demuyakor, 2020; Händel et al., 2020; International Association of Universities, 2020). The result, called emergency remote teaching (ERT, Hodges et al., 2020), included a great amount of improvisation and ad hoc strategies that need to be contrasted to well-planned online learning scenarios (e.g., Rapanta et al., 2020). The initial emergency state has since transitioned into the post-pandemic or post-COVID classroom ( Curtin, 2021), in which higher education institutions have the opportunity to integrate those remote teaching practices which have proven their worth into thoroughly planned online or blended learning arrangements while refining or omitting ineffective practices.

In ERT, almost all face-to-face teaching was substituted through online teaching formats ( Zawacki-Richter, 2020; Cicha et al., 2021; Goertz and Hense, 2021). This transition was accompanied by the awareness that the pedagogy needed to be adapted to the new medium in the sense that simply moving pedagogy from one medium into another was not enough to ensure quality learning ( Henriksen et al., 2020). In addition, students and teachers not only needed new skills in handling technology but also in interacting with each other, resulting in newly shaped roles ( Coppola et al., 2002; Arbaugh, 2004; Granitz and Koernig, 2011; Blumentritt et al., 2020). During the pandemic, the social aspect of university learning was especially challenging, resulting in reports of anonymity and a lack of social presence. In a study prior to the pandemic, Daigle and Stuvland (2020) found this lack to account for differences between modalities regarding, for example, lower satisfaction with online learning. They described this as the social presence gap and claimed that teachers should invest in overcoming this gap to equalize outcomes across modalities. For many students, the unaccustomed distance in their learning was challenging, for example, Bedenlier et al. (2020) found that students felt uncomfortable using their webcams in synchronous settings. The authors attributed this to the unfamiliar setting, in which they constantly see themselves, and it remains unclear who can see them. Also, students perceived diffuse relationships to their peers and were less likely to experience social support in settings of online learning compared to traditional settings ( Bedenlier et al., 2020). In addition, many students also reported an increased workload ( Aristovnik et al., 2020). Overall, these findings stress the importance to carefully consider students' learning experience when tackling the question of how to engage them in online learning.

In online learning, two basic settings are often compared, asynchronous and synchronous. They differ in terms of time and place of teaching and learning activities: Asynchronous settings are temporally and geographically independent and defined as more individually based and self-paced as well as less instructor-dependent ( Bernard et al., 2004; Murphy et al., 2011; Clark and Mayer, 2016; Xie et al., 2018). They, however, also bear challenges, as also implied by the media richness ( Daft and Lengel, 1984; Blau et al., 2017) and media naturalness ( Blau et al., 2017) approaches. The media richness approach describes the "capability of a medium to (1) provide immediate feedback (2) transmit verbal and non-verbal communication cues (3) provide a sense of personalization, and (4) simulate a natural language" ( Blau et al., 2017), whereas for the degree of medium naturalness, face to face is considered to be the most natural form of communication. This results in synchronous learning environments to be less natural and less "rich" than face-to-face synchronous learning environments. The authors therefore claim that this leads to higher cognitive load, greater communication ambiguity, and lower activation. And albeit asynchronous teaching can enable students to work self-paced and independently of time and place ( van der Keylen et al., 2020), not all learners are equipped with the according strategies to benefit from this potential advantage: Learning at home, especially in asynchronous contexts, requires more self-study skills to stay on track, including enough motivation and will to follow learning goals ( cf. Hartnett, 2015). Also, students must be equipped with strong digital skills to perform academic work and successfully complete learning activities ( Kim et al., 2019).

The main strengths of synchronous online learning are the real-time interpersonal communication, the use of natural language, and immediate feedback ( Blau et al., 2017). These attributes can diminish the difference between online and face-to-face learning in this manner and provide a sense of personalization. In contrast, synchronous communication has been found to be less useful for discussing complex ideas or deep reflection (for a review, see Hrastinski, 2010). For students, learning experience, positive outcomes, and the type of performance matter: They acquire practical skills better when they are taught in a synchronous online setting ( Nsa et al., 2012; Ogbonna et al., 2019), whereas cognitive achievement, such as producing meaningful and thoughtful contributions, is greater in asynchronous settings ( Hrastinski, 2008; Garrison, 2011; Ogbonna et al., 2019). Also, synchronous learning positively impacts learners' commitment and their task motivation ( Hrastinski, 2008). At the same time, similar to face-to-face settings, the danger of disengaged participation in class (e.g., passive listening or watching the teacher's lecture, silently reading peer statements in the chat) has to be considered ( Smith and Smith, 2014). According to an interview study with experts on online teaching by Rapanta et al. (2020), videoconferencing decreases the fluency of interaction and makes interactions slower and attention lower compared to traditional teaching ( Rapanta et al., 2020). Another challenge of synchronous learning relates to the technical infrastructure that has to allow for participation in live remote settings in a sufficient quality (i.e., internet bandwidth; Xie et al., 2018).

Research findings regarding the impact of synchronous and asynchronous teaching settings on student performance are not without ambiguity. Nieuwoudt (2020) found that it did not make a difference for student achievement whether students attended synchronous virtual classes or watched the recordings of the virtual classes. However, the sheer time students participated in and interacted with the online learning system did significantly affect their academic success. Also, active participation in both synchronous and asynchronous online learning opportunities has been found to result in higher engagement and better academic outcomes than attending face-to-face classes only ( Northey et al., 2015).

In order to scrutinize the impacts of synchronous and asynchronous online teaching and learning on student variables, it is necessary to consider the role of specific teaching methods and the underlying pedagogy of the online courses ( Murphy et al., 2011). Synchronous and asynchronous settings differ in the choice of tools used and their pedagogical objectives. Xie et al. (2018) identified five variables to differentiate between synchronous and asynchronous settings: communication tools, feedback types, input methods, collaboration modes, and the skills targeted. The researchers find that while students are more satisfied with asynchronous communication tools (such as discussion forums or email communication), they also appreciate the possibility of direct instructor feedback in synchronous settings. Also, both the quality of learner-content interaction (i.e., reading interactive texts, watching videos, and completing assignments), and learner-teacher interaction (i.e., providing feedback, providing summative and formative assessments, and documenting students' progress) have a strong effect on satisfaction with learning and perceived learning, especially in asynchronous formats ( Kuo et al., 2014; Nandi et al., 2015; Alqurashi, 2019; Fredericksen et al., 2000). Activities, such as online discussions, are perceived as more individualistic and less cooperative by students in asynchronous compared to synchronous settings and are also associated with greater negative effects and a decreased sense of belonging ( Peterson et al., 2018). In contrast, learners characterize participation in online synchronous discussions as more focused, having a stronger sense of contribution, increasing motivation, and supporting better course performance than asynchronous discussions ( Chen and You, 2007; Hrastinski, 2008, 2010; Malkin et al., 2018). Discussing teaching and learning methods to facilitate communication within synchronous and asynchronous educational settings, researchers stress the necessity to differentiate between various types of activation and interaction and ways how students are engaged in the learning process as more crucial for study success compared to the form of course delivery ( Zhu, 2006; Skylar, 2009; Nieuwoudt, 2020; Rapanta et al., 2020; Sweetman, 2021).

Applying criteria for interactivity, teaching and learning methods can be classified in methods with higher versus lower interaction potential. Interactivity in this context refers to the possibility for learners to be socially and cognitively engaged in (1) interaction with content through learning materials, (2) interaction with peers, and (3) interaction with teachers ( Anderson, 2003). According to this classification, collaborative formats as discussion, feedback, and working in small groups have higher potential to support social interaction and engagement of students in contrast to lecturing, self-assessments, or individual work which have higher potential for content-oriented interaction in online learning ( Rapanta et al., 2020). Similar aspects of student activation and interaction are considered in a well-established classification of student-centered and teacher-centered teaching and learning methods that are usually linked to different degrees of active or correspondently passive learning ( Kain, 2003; Chi, 2009; Biggs and Tang, 2011; Wright, 2011) including online learning as well (e.g., Reaburn et al., 2009). A distinguishing parameter of asynchronous versus synchronous online learning is the prevailing learner-content ( via learning materials) interaction in asynchronous settings in comparison with learner-instructor or learner-learner interaction ( Alqurashi, 2019).

Engaging students in online learning is considered a pivotal prerequisite for their success ( Chiu, 2021). Also, learners' motivational characteristics, such as technology acceptance, are often considered factors that can influence achievement or learning satisfaction in synchronous versus asynchronous online courses. The self-confidence in utilizing technologies used in the online course or communicating with a teacher or peers via tools is strongly linked to perceived learning and satisfaction ( Shen et al., 2013; Alqurashi, 2016; Malik and Fatima, 2017). In general, the facets of technology acceptance - perceived ease of use (PEOU) and perceived usefulness - are considered significant factors for adopting online teaching and learning environments (see Šumak et al., 2011 for a meta-analysis), irrespective to the type of online resource (e-learning system or single e-learning tool/technology). Recent studies add evidence on the role of technology acceptance in adoption of specific technology-based activities such as online collaboration for problem-based scenarios ( Cheung and Vogel, 2013). Very few studies pay attention to the role of technology acceptance in utilizing online learning under the circumstances of the COVID-19 pandemic (i.e., Cicha et al., 2021) showing new patterns of interrelations between technology acceptance, computer anxiety, and self-efficacy.

To investigate prerequisites for learning motivation in synchronous and asynchronous online learning, the self-determination theory (SDT, Ryan and Deci, 2000) presents a befitting framework ( Hartnett, 2015; Chiu, 2021). SDT argues that three fundamental psychological needs have to be satisfied for people to act intrinsically motivated in a given environment and to engage with learning: First, people need to feel self-determining or autonomous in their decisions and, through this experience, a sense of control. Second, they need to feel competent or capable to comply with the demands of a given task. Third, they have to feel socially related to or included in a group of others. If a learning context satisfies these basic psychological needs, learners are likely to act intrinsically motivated by, for example, engaging actively in the learning tasks, showing enhanced performance and demonstrating greater endurance when faced with obstacles ( Schunk et al., 2014). The key concept for supporting motivation in SDT is the social context. In learning settings, social interactions with the teacher and fellow students can all provide the basic needs of autonomy, competence, and relatedness. One of the benefits of SDT is that it equips teachers with practical advice regarding the kinds of social interactions that students need in order to provide sufficient support for all three basic needs (e.g., granting choice regarding contents or the execution of tasks, offering informational feedback, and assigning group tasks; e.g., Reeve and Jang, 2006). SDT has been successfully applied to classic face-to face-educational settings ( Niemiec et al., 2006): Previous studies show that SDT can predict a range of learning outcomes, such as performance, persistence, and course satisfaction ( Deci and Ryan, 1985). The social context of online learning differs fundamentally from that of traditional face-to-face learning: Communication takes place through video conferencing tools, forums, chat tools, or email in asynchronous settings, because learners and teachers in online settings of teaching and learning are separated by time, distance, or both. Thus, it seems reasonable to pay special attention to the social context when investigating the link between online learning and teaching settings and learning motivation. For example, previous studies have shown that lack of teacher input, not having a genuine reason to communicate online with peers, low self-efficacy, and time and technology constraints can lower motivation ( Xie et al., 2006; Artino, 2007; Cheung et al., 2008; Moos and Azevedo, 2008; Hartnett et al., 2011). By emphasizing the importance of the social context for motivation, SDT is particularly suited to the context of online learning. Some research has previously applied SDT to online learning and learning: A recent study by Chiu (2021) investigated how SDT could explain engagement of students in high school during COVID-19 and found that especially the support of relatedness was important. Also, Hartnett (2015) adopted SDT to an online environment and identified several influences that might undermine the psychological needs: high workload, assessment pressure, perceptions that the learning activity lacked relevance (autonomy-undermining), unclear and complicated guidelines, insufficient guidance and feedback from the teacher (competence-undermining), and communication issues with peers (relatedness-undermining). Chen and Jang (2010) used structural equation modeling to test a model for online learner motivation based on SDT. While they found support for the association of contextual support, satisfaction of the three basic needs and student motivation, self-reported motivation failed to predict learning outcomes. However, in a similar approach, Hsu et al. (2019) showed that satisfying the basic needs enhances self-regulated motivation, which is associated with higher perceived knowledge transfer and increased achievement of course objectives. Various studies showed that self-reported student motivation is positively associated with the quantity as well as quality of learning behavior in online teaching and learning settings, such as actively posting messages to an online learning platform ( Xie et al., 2006; Hartnett, 2012). Xie et al. (2006) also found that student motivation is associated with teacher behavior, as for example, participation, guidance, and feedback.

The present study investigates how synchronous and asynchronous settings of teaching and learning during the 2020 lockdown affected student learning experience, including learning motivation, but also general satisfaction, learning behavior, and reported learning outcomes. The presented prior research on synchronous and asynchronous online learning stressed potentials and challenges of either setting, leading us to a partly explorative approach in this research to be able to provide a description of how synchronous and asynchronous teaching and learning settings in ERT were characterized by students and teachers regarding the applied teaching methods. A potential distinguishing factor between synchronous and asynchronous teaching and learning is how they facilitate social interaction between agents, why we chose to explore whether the settings differed in teaching methods and whether prerequisites for students engagement as proposed by SDT, ( Ryan and Deci, 2000) are met differently between settings. Summarizing the above-mentioned studies on factors influencing online learning, we can classify them mainly in three groups - (1) learner-related variables (i.e., satisfaction, needs, and skills) (2) learning environment-related variables (i.e., synchronicity and potential for interactivity of online courses), and (3) teacher-related variables (i.e., applied teaching methods and teaching practices). Overall, we assume that a greater fulfillment of SDT needs should be associated with as more positive learning experience, as for example, a higher satisfaction with online learning and a higher reported support of SDT needs. Also, we assume that students who are more likely to accept online tools as useful and easy to use experience online learning during the pandemic as more positive.

Therefore, the following research questions frame our study:

Q1 a How are synchronous and asynchronous teaching and learning settings characterized by students and teachers regarding the applied teaching methods?

Q1 b Based on the proposed classification of methods regarding their potential to facilitate social interaction: What types of interaction are promoted in synchronous and asynchronous teaching and learning settings as reported by students and teachers?

Q2 Do students who experienced mostly asynchronous online teaching and learning report different overall evaluations of the online semester, fulfillment of basic psychological needs (SDT) as well as different learning gains compared to their peers who experienced mostly synchronous online teaching and do the teachers' views validate students' evaluations?

Q3 Is a more positive learning experience (overall evaluations of the online semester, self-reported learning gain) associated with

a) greater fulfillment of students' basic psychological needs proposed by SDT?

b) greater acceptance of online tools?

Materials and Methods

Sample

The study reports data from both a student and a teacher online survey from a large German public university. The surveys were initiated by the university's department of teaching and quality assurance in collaboration with representatives of other departments associated with teaching and learning. About 46,000 students are enrolled at the university, which employs about 3,500 research and teaching faculty. For the surveys, a randomly selected 50% percent of the student body and the teaching faculty were contacted, while making sure that teachers and students from all faculties received invitations. The other 50% of students and faculty were invited to participate in another survey focusing on examinations during COVID-19, the results of which are not part of the present paper. A total of 3,056 students completed the survey (return rate=15%, female=65.8%) as well as 396 teaching faculty (return rate=33%, female=39.1%). Table 1 contains further information about the student and teacher samples, including disciplinary clusters and students' expected degrees. Both groups showed representativeness for the disciplines involved. On average, participating students were enrolled in their 4.9th semester ( SD=3.34), and teachers reported an average number of 20.01 semesters ( SD=17.43) of teaching experience. Note that students and teachers represent independent samples within the university and are not matched.

Context of the Study

The surveys aimed to provide the university with a comprehensive feedback from students and teaching faculty on their experiences with the first online study term during the 2020 lockdown in Germany. This paper mainly reports select results from the student survey, but also refers to additional variables from the teacher survey to add a complementary perspective.

Material

The student and the teacher surveys were carried out in German and were administered using EvaSys 7.0 software. The participation was voluntary and not linked to any credit. After providing their informed consent, participants anonymously answered the survey questions. Data were collected after the lecture period of the summer term; the survey was online from August until mid of September in 2020. All data were handled confidentially and securely on EvaSys and archived on a password-protected server. Due to the overall length of the surveys, all applied scales had to be shortened and were also adapted to fit the context of the study; other variables were measured through single items only. This article focuses on a number of selected variables that will be explained in further detail in the following.

Student Survey

The student survey was designed to cover students' views on the first online semester during the 2020 pandemic. It comprised background variables as well as evaluations of their study experience.

Teaching and Learning Methods

Students were asked to rate the frequency (1= never to 4= very frequent) of 11 different teaching and learning methods across all their courses. Teaching and learning methods were identified based on Alqurashi (2019) and included synchronous and asynchronous activities as well as methods that could be used in either setting (see Table 2). Following approaches differentiating learning activities in accordance with interaction types ( Anderson, 2003; Chi, 2009), we propose a classification aimed to classify teaching and learning methods regarding their potential to facilitate social interaction (comprising learner-learner and learner-teacher interaction, see Table 2).

Individual Assessment of the Study Term

In single items, students were asked to evaluate their overall satisfaction with the online term (1= strongly disagree to 6= strongly agree), whether they experienced - in comparison with traditional teaching - additional strains (1= strongly disagree to 6= strongly agree) and additional workload through the online teaching. Students were also asked in which ratio they experienced synchronous teaching and learning across all their courses (1= all synchronous to 5= all asynchronous). Following the operationalization by Murphy et al. (2011), synchronous online teaching was understood as a temporally dependent arrangement between students and teachers, defined as weekly courses with fixed timeslots, whereas asynchronous teaching was defined by the absence of fixed weekly time slots, that is, temporally independent.

Self-Determination

To assess the perceived fulfillment of the base psychological needs proposed by SDT, we applied a questionnaire by Rösler et al. (2016). In three subscales, autonomy support, competence support, and relatedness were assessed by three items each:

*Autonomy support: (1) I was able to complete assigned tasks my way. (2) I was able to manage time in my studies myself. (3) I had the opportunity to engage with contents I found interesting more intense.

*Competence support (1) I received clear and detailed feedback on my learning results. (2) I was provided with distinct information on how to improve. (3) When there were difficulties, I was able to get support at any given time.

*Relatedness (1) Overall, I experienced a feeling of belonging in my virtual courses. (2) The atmosphere amongst students was friendly and relaxed. (3) I felt comfortable amongst my fellow students.

All items were answered on a 6-point rating scale (1= does not apply to 6= fully applies; autonomy support: α =0.75; competence support: α =0.86; relatedness: α=0.81).

Learning Gain

Students were asked to rate their overall gain in five distinct learning areas: content-related skills, method-related skills, digital skills, content interest, and autonomous learning (1= very little to 6= considerably).

Learning With Digital Tools

To assess the quality of learning with digital tools, we included a single item: Whether the constant availability of learning material led students to procrastinate (1= does not apply to 6= fully applies). We also included a shortened version of the learning content interaction subscale from a questionnaire by Alqurashi (2019) to measure how students judged their learning with online material (learner-content interaction, LCI). The three items were rated from 1= does not apply to 6= fully applies ( α=0.91): (1) O nline course materials helped me to understand better the class content. (2) Online course materials stimulated my interest for this course. (3) Online course materials helped relate my personal experience to new concepts or new knowledge. Referring to the Technology Acceptance model ( Davis, 1989; Davis et al., 1989), we assessed the perceived ease of use (PEOU) as well as the perceived usefulness (PU) of online tools in teaching by each two items, that were answered on a 6-point rating scale (1= does not apply to 6= fully applies (PEOU: α =0.82; PU: α =0.85).

*Perceived ease of use: (1) I find the online tools in teaching easy to use. (2) I find online tools in teaching to be flexible to interact with.

*Perceived usefulness: (1) Using online tools in teaching makes my learning more effective. (2) I find the online tools in teaching useful in structuring my learning.

Teacher Survey

From the comprehensive teacher survey, we focus on the following selection of single items:

Evaluation of own teaching. Teachers were asked to rate their overall satisfaction with the online term (1= strongly disagree to 6= strongly agree) and to compare the effort to prepare and perform teaching with their usual experience (1= far less to 6= far more). Furthermore, they were asked to state whether their digital competences enhanced during the online semester (1= very little to 6= considerably). As with the students, teachers were asked to rate whether they taught more synchronously or asynchronously on a 5-point rating scale (1= all synchronous to 5= all asynchronous) as well to rate the frequency (1= never to 4= very frequent) of teaching and learning methods across all their courses (see Table 2).

Evaluation of student variables. Teachers were asked to rate whether students seemed to be more burdened in this semester than they usually are, whether students seemed to be overwhelmed by the number of digital tools, and whether the teacher thought that most of their students had problems in organizing their own learning at home. All three items were answered on a 6-point rating scale from 1= does not apply to 6= fully applies.

Analyses

Based on the nature of our research questions, we included descriptive analyses (Q1a and Q1b), as well as analyses of group differences (Q1a, Q1b, and Q2), and the evaluation of associations between variables (Q3). To address possible group differences, we computed univariate ANOVAs. For associations between variables, we applied two-sided Pearson's correlations. Data were analyzed using SPSS (Version 26).

Results

In preparation to further analyses, we dichotomized synchronicity of teaching for students and teachers to enable a comparison of extreme groups. For both samples, we merged the lower two (= mostly synchronous, students; n=1,020; 33.4%; teachers: n=149; 37.6%) and the upper two values (= mostly asynchronous, students: n=825; 27%; teachers: n=130; 32.9%) while omitting the middle category (= "a bit of both," students: n=999; 32.7%; teachers: n=100; 25.3%)

(1a) To answer the research question how synchronous and asynchronous teaching and learning settings are characterized by students and teachers regarding the applied teaching methods, we first viewed the reported frequencies as a function of the two teaching and learning settings (see Figure 1, for the exact descriptive statistics, see Tables 3, 4). The descriptive results show that lectures and presentations were by far the most common method - videotaped for the mostly asynchronous groups and live via videoconferencing for the mostly synchronous groups. Unsurprisingly, practical work was reported as least frequent in all groups. We followed up with a more detailed analysis of the descriptive results.

Figure 1. Reported mean frequencies of teaching and learning methods (1= never to 4= very frequent) in synchronous vs. asynchronous settings by students and teachers.

We conducted two univariate ANOVAs to test the assumption that the frequency of reported teaching and learning methods is dependent on the synchronicity of courses participated in (for students) or conducted (for teachers). The results revealed that students in the mostly asynchronous group reported significantly more recorded lectures or student presentations, as well as more discussions via online forums (LMS), with both methods being an integral part of the concept of asynchronous settings (see Table 3). Students in the mostly synchronous group reported significantly more lectures or student presentations via videoconferencing as well as more discussions via chat tools or breakout rooms, with both methods being an integral part of the concept of synchronous settings. As expected, students experiencing mostly synchronous settings also reported significantly more practical or lab work. They also reported higher frequencies for all the three forms of feedback activities (peer feedback, teacher feedback, and student feedback to the teacher) which are not conceptually tied to a specific setting. No significant differences could be found in reported frequencies of group work, self-assessments, and (online) office hours between synchronous and asynchronous groups.

In addition, teachers in the mostly asynchronous group reported high frequencies of recorded lectures or student presentations and organizing discussions via forum (LMS; Table 4). Additionally, the offer of online office hours was significantly higher in the mostly asynchronous group compared to the mostly synchronous. Similar to the student perspective, lecturing and organizing discussions via videoconferencing were also perceived by teachers in the mostly synchronous group as significantly more prevalent. No significant differences from the teachers' perspective could be found for the reported frequencies of group work, self-assessments, and practical work/laboratories as well as for all three types of feedback (peer feedback, teacher feedback, and student feedback to the teacher) between synchronous and asynchronous groups.

Thus, students and teachers perceived the teaching and learning methods in synchronous and asynchronous settings differently: Teachers perceived fewer difference between teaching and learning methods in synchronous and asynchronous settings compared to students, especially in relation to feedback activities, which students reported as more frequent in synchronous settings. Another difference relates to (online) office hours that teachers offer more frequently when they teach more asynchronously compared to the mostly synchronous group. Here, students reported no difference

(1b) To answer the research question concerning prevailing types of interaction (lower vs. higher potential to facilitate social interaction) in synchronous and asynchronous teaching and learning settings, we qualitatively analyzed the reported teaching and learning methods, based on the proposed classification of their potential to facilitate social interaction displayed in Table 2. In summary, students in the mostly synchronous group experienced more teaching and learning activities with higher potential to support social (practical or lab work as well as the three types of feedback activities) as opposed to methods with lower potential to support social interaction (e.g., lectures via videoconferencing). In contrast, students in the mostly asynchronous group reported more methods with lower potential to facilitate social interaction (e.g., tests and recorded lectures) as opposed to methods with higher potential to support social interaction (e.g., forums and feedback activities). At the same time, teachers perceived teaching and learning methods in both learning environments as balanced in facilitating all types of interaction

(2) Regarding the second research question, student variables on the individual learning experience, SDT, and the reported learning gain were compared for group differences. For an overview, descriptive results of student variables are displayed in Table 5 across all students together with their intercorrelations. Almost all of the intercorrelations are significant indicating a likely overall factor behind the student ratings.

Table 6 displays the descriptive results for the two student groups with primarily synchronous and asynchronous teaching. Descriptive statistics for the selected teacher variables can be found in Table 7. The results suggest an overall medium to high satisfaction in both groups but also relatively high absolute ratings for additional strains and additional workload. To test for significant group differences, we conducted a univariate ANOVA for the student variables (see Table 6 for a summary of results) as well as one for the teacher variables (see Table 7). While the focus lied on students' results, we report corresponding results from the teacher survey to add another perspective wherever possible. Students in mainly synchronous settings were significantly more satisfied with teaching across all their courses. It may be interesting to add that teachers mostly involved in synchronous settings were themselves more satisfied with the online term than those teaching in mostly asynchronous settings were. Students in mostly asynchronous settings reported a higher additional workload compared to teaching in face-to-face settings than did their peers in the synchronous group. We also found a significant difference between the two groups of students in terms of the perceived additional strains during the online term, even though the question was not directly related to teaching scenarios. Students in the asynchronous group report higher scores, which is also confirmed by the corresponding result from the teacher survey (see Table 7). For the SDT-related variables, we find significant differences between the two groups with higher values for competence support and relatedness in the synchronous group and higher values for autonomy support in the asynchronous group. The group with mostly synchronous teaching also reports significantly higher ratings in gaining procedural and social skills, as well as in their interest in the disciplinary content. Students with mostly asynchronous teaching report greater gains in self-directed learning compared to the other group. No differences were found in students' learning gains regarding content skills, vocational skills, and digital skills. About half of teachers reported that most of their students had problems with self-organizing their learning at home

(3) To answer Q3, we refer to the correlational data reported in Table 5. We were interested in whether higher values in SDT as well as in PEOU and PU are associated with a more positive learning experience and can therefore act as protective factors for students. For these analyses, we refer to the complete set of students' data. Following the assumptions of SDT, we expected that students whose basic psychological needs of autonomy, competence, and relatedness were more satisfied in the online semester also report greater overall satisfaction with the online semester as well as greater learning gains. Correlations between students' overall satisfaction and the three basic needs range from 0.48 to 0.56 and were all significant, confirming our expectation. Correlations between the three basic needs and self-reported learning gains were also all positive and significant, ranging from 0.30 to 0.57, with the associations between the perceived support of autonomy and the different kinds of self-reported learning being the strongest. All three basic needs were also significantly negative associated with perceived additional strains during the online semester as well as with procrastination behavior. We further assumed that high technology acceptance should ease students learning experience in the online semester. Correlations between PEOU and student variables ranged from 0.17 to (−)0.43 and were all significant ( p<0.001), with the highest coefficients for the association with overall satisfaction ( r=0.43), LCI ( r=0.40) and autonomy support ( r=0.42). The perceived usefulness of online tools showed correlations between r=0.28 and r=0.66. All correlations were significant ( p<0.001), and none of the directions was counterintuitive. Yet, we only found moderate to strong correlations. Students high in perceived usefulness judged their overall satisfaction with the online term positive as well ( r=0.66) and reported less additional strain ( r=−47). PU also positively correlates with higher perceived quality of learner-content interaction (LCI, r=0.66) as well with the three SDT needs (autonomy support: r=0.59; competence support: r=0.48; relatedness: r=0.53). Moderate positive correlations occurred also with reported learning gains for content skills ( r=0.56), method skills ( r=0.49), vocational skills ( r=0.4), interest ( r=0.55), and autonomous learning ( r=0.46).

Discussion

Through the work presented in this article, we aim to understand better, how university students and teachers experienced different settings of online teaching and learning during the online semester due to the COVID-19 lockdown. In particular, this study aims to comprehend the effects of mostly synchronous and mostly asynchronous teaching and learning settings on students and at providing insight into possible implications for future online teaching and learning in higher education. Based on the results of a university-wide survey, we analyzed whether synchronous and asynchronous teaching and learning settings were associated with different teaching methods as well as differences in various student variables.

Discussion of Results
Teaching and learning activities in synchronous and asynchronous setting involve less interaction inducing methods than input methods.
The first research question explores which teaching methods were reported by students (and teachers) who experienced mostly synchronous or asynchronous online teaching and learning. Results show that considerable groups of students experienced teaching that was predominantly either synchronous or asynchronous. Only about one-third of students reported equal ratios of both settings. Even though a wide variety of methods was reported, results show that synchronous and asynchronous online courses were dominated by prepared inputs by students, teachers, or both, such as live presentations during video conferencing or previously recorded lectures or screencasts.

However, the frequency of the methods reported by students and teachers depended on the synchronicity of the courses. Unsurprisingly, students and teachers who studied or taught mostly asynchronous reported more methods that are conceptually tied to asynchronous settings (e.g., recorded lectures or student presentations and discussions via online forums) compared to students and teachers in mostly synchronous settings. Vice versa, students and teachers in mostly synchronous settings reported more synchronous methods (e.g., presentations via videoconferencing, discussions via chat tools or breakout rooms) compared to students and teachers in mostly asynchronous settings. These results were expected because methods such as recorded lectures are inevitably applied more often in asynchronous settings while videoconferencing can only be realized in synchronous settings. Nevertheless, these results may serve as confirmation that our segmentation of the sample into a mostly synchronous group and a mostly asynchronous group was admissible.

Our findings reveal discrepancies regarding student and teacher perceptions of the frequency of methods that facilitate interaction in synchronous and asynchronous settings.

It has to be noted that synchronous and asynchronous settings differ in principle regarding their potential to facilitate social interaction: Synchronous environments allow for teaching methods such as group work or video discussions, which inherently support social interaction of students as well as student-teacher interaction. In comparison, asynchronous environments are more content-oriented and teaching methods conceptually tied to asynchronous settings have a focus on facilitating student interaction with the learning materials. Asynchronous methods that facilitate social interaction such as discussions in online forums require more attention as well as a more thorough planning in order to support social interaction compared to for example discussions in video conferences. However, all three forms of feedback activities (peer feedback, teacher feedback to students, and student feedback to the teacher) can be realized in both synchronous and asynchronous settings. Yet, our data suggest that students in mostly synchronous settings experience more feedback compared to students in mostly asynchronous settings.

Interestingly, the students' perception of feedback activities in synchronous and asynchronous settings in our study is not confirmed from the teachers' perspective: Teachers reported to apply all three feedback activities (as well as group work and practical work/labs) equally in both asynchronous and synchronous settings. One likely explanation for this discrepancy is that teachers are just not aware that they allow for less feedback in asynchronous settings compared to synchronous settings. Maybe some of the feedback activities that take place in synchronous settings occur unintentionally without being deliberately planned by teachers. In any case, given the pivotal role of informative feedback in (not only) higher education learning in order to assure motivation and learning outcomes ( Biggs and Tang, 2011; Hattie, 2011), this finding may suggest a disadvantage for students experiencing mostly asynchronous teaching and learning settings. Similar differences in teacher and student perceptions were found earlier regarding preferences for interaction-based and input-based settings by Struyven et al. (2008). The authors found that these preferences were able to influence students' overall perceptions of learning environments as well as their learning strategies and their performance, while it is known that for learning success, input formats usually depend on both attention and interest from the students ( Rapanta et al., 2020).
Students in synchronous settings report a more positive learning experience as well as greater support of their basic psychological needs.
The second research question compares the two groups' learning experiences. We find satisfaction rates for synchronous settings to be higher, indicating that the social aspects of teaching and learning (e.g., feedback and interaction), which from the students' perspective are more prevailing in synchronous settings, play an important role for student satisfaction. Regarding the support of the three basic psychological needs as described by SDT, our presumption is confirmed that students' needs to feel competent as well as socially related cannot be taken for granted, especially for asynchronous settings. This study thereby contributes further empirical evidence for the appropriateness of applying the SDT to online teaching and learning in higher education. Future research that systematically varies teaching methods could provide further insight as well as intervention studies in which teachers are trained to apply the principles suggested by SDT in their teaching.

Regarding the students' self-reported learning gains, synchronicity of the online setting seems to be of minor importance: While unsurprisingly, a majority of students reported improving their digital skills - as did more than 80% of the teachers - there was no difference between synchronous and asynchronous settings. Likewise, students self-reported learning gains did not significantly differ with regard to content-related skills and vocational skills. However, students who experienced mostly asynchronous teaching report greater gains in autonomous learning and smaller gains in social skills, both results being immediately plausible since asynchronous settings are characterized by high degrees of autonomy and fewer possibilities for social exchange. In contrast, students who mostly experienced synchronous teaching reported a greater increase in interest in the course content than students in asynchronous settings, suggesting that the content-related exchange with others supports the evolvement of interest for a certain topic. In addition, students in mostly synchronous settings reported higher gains in methodological skills. These results complement the findings by Nguyen (2021), who found that students prefer synchronous settings. While these results suggest a superiority of synchronous teaching and may be interpreted in such way that more video conferences are needed in higher education, one could also conclude that for the particular case of emergency remote teaching due to the COVID-19 pandemic, teachers had difficulties tapping the full potential of asynchronous teaching and learning arrangements. With more time for thorough course planning, teachers have the possibility to incorporate intelligent opportunities for both teacher-student and student-student interactions and collaboration into their online courses ( Alqurashi, 2019). In this sense, results should be used to optimize both types of learning arrangements and allow for their purposeful use. Hrastinski (2010) suggests that synchronous communication may be used to foster personal participation and to allow convergence on meaning as well as provide task-related and social support, especially when applied in smaller group settings and for less complex tasks. Also, according to Daft and Lengel's (1984) media richness theory, media Daft and Lengel (1984), mediums differ in their capability to transmit information with while face-to-face communication being the richest medium. Reflected knowledge of the different capabilities of different media should allow teachers to rationalize their choices to enhance their students' learning.
Overall, greater fulfillment of psychological needs and higher acceptance of online tools go along with a more positive learning experience.
The third research question investigates whether higher SDT values were also associated with a more positive learning experience and whether greater technology acceptance also served as a protective factor for students in that sense. Indeed, we found that higher satisfaction scores regarding the three basic needs according to SDT correlated positively with overall satisfaction and negatively with the perception of additional strains and reported procrastination. The differences between synchronous and asynchronous settings stress the importance of the support of relatedness (see also Chiu, 2021), to make up for the disadvantages that go along with asynchronous settings. Similar to the results by Hsu et al. (2019), we also found that needs fulfillment were positively correlated with all of the facets of self-reported competence gain. Together with the results from our second research question, this indicates that the satisfaction of basic psychological needs enhances students' learning experience comprising higher satisfaction, less procrastination, and greater learning gains. At the same time, students reported more support for their three basic needs in synchronous learning settings. Aside from synchronicity, we also found a positive correlation between autonomy support and the PEOU of technology. It could be argued that through this, also the interaction with online learning content could be eased, resulting in the experience of more autonomy support. With these results, this study contributes to the existing evidence for the application of SDT in online learning and it provides a good starting point for theoretical and practical implications. Even though SDT-related results in this study may suggest that synchronous settings outperform asynchronous settings, there are many good reasons why higher education should not completely abandon asynchronous teaching and learning. In the correlative results, we found strong associations between the perceived usefulness of given online tools and a positive learning experience, implying that teachers in general should allow their students to experience the usefulness of the chosen tools.

Limitations

Several limitations of the current study should be noted. As many other studies on experiences with remote learning due to the pandemic, the results rely on data that are derived from a single German university; therefore, the results can only be generalized to a limited extent. However, the university is large and includes a wide variety of disciplines and study programs. Universities in Germany are equipped similarly when it comes to basic infrastructure and the challenges of the COVID-19 pandemic created a comparable interruption of regular teaching and learning for everyone. Therefore, we assume that results should be transferrable, at least for the German context. The relatively low response rate might also have resulted in a self-selection bias of students and teachers with regard to possible systematic differences to the non-responding groups. The representativeness for the faculties still is encouraging as well as the variance in variables' scores. Also, the SDT describes the needs as universal across individuals ( Deci and Ryan, 2000). From this point of view, the aggregation of data across courses and disciplines as well as grouping teachers and students according to the synchronicity of online learning can compensate the absence of matching between student and teacher samples on the course level. Another challenge is the quality of data, in regard to known problems of self-report measures, which are susceptible to memory distortions and do not equal actual performance ( Schellings and van Hout-Wolters, 2011). And while, as mentioned by Pekrun (2020), self-reports can deliver data of high validity in investigating motivational, cognitive, or emotional aspects of learning but they should be enhanced by other data sources. Albeit the validity of the data was partially increased be integrating responses from teacher survey and student survey - allowing to some extent the cross verification of the findings from teacher and student perspectives, it would still be desirable in the sense of data triangulation for future research to integrate other sources of data related to online learning. These could include, for example, the frequency and real-time use of LMS, chats, or videoconferencing as well as the number of downloads of recorded lectures or podcasts. Another possibility of data triangulation could be a better integration of qualitative data in addition to quantitative data enabling stronger validation of results. As a further limitation, it should be mentioned that in student evaluations of teaching, high intercorrelations are well-known, indicating a central factor that influences a student's evaluation of the lecturer ( Shevlin et al., 2000). Still, self-reports provide an opportunity for insight into cognitive, motivational, and behavioral processes on a broad level that can help to detect systematic correlations. Another limitation is the instruments used for this study: Scales had to be shortened in order to be included in the comprehensive student and teacher survey. Some information had to be collected through single-item measures. However, the internal consistencies of scales applied were good or very good and therefore ensure a certain psychometric quality. A general challenge of one-shot studies is that they only have a correlational scope and do not allow causal relationships to be established even if the theoretical assumptions suggest them. And while we were able to harness data from both the student and teacher surveys, we are unable to link both data sources so we do not know whether potential differential effects are covered. All of these limitations connote future research strategies, where, for example, fewer courses are researched in more depth.

Conclusion

Overall, our findings contribute to theory because they further indicate that the synchronous and asynchronous settings are no uniform environments but offer a variety of different options for teaching and learning. Also, our results offer evidence for an association between these settings and prerequisites for student engagement and indicators for satisfaction and learning behavior and perceived learning outcomes. Our research focused on teaching and learning during the 2020 lockdown, but even if the post-COVID classroom will differ from the ad hoc circumstances experienced during the first lockdown, the experience has produced a vast amount of insights into opportunities, potentials and risks of digitally organized learning ( Aristovnik et al., 2020). These highly valuable first-hand experiences with online teaching and learning under real life conditions need to be integrated with existing findings from systematic research on online learning to help to refine future higher education online teaching and learning. However, it should be kept in mind that cultural differences might affect learning experience when interpreting findings that stem from specific national contexts ( Chiu, 2021). We have found SDT to serve as a valuable model in interpreting results, and we would encourage further research to add to empirical evidence of SDT in higher education and specifically in online learning.

The universal necessity to engage with online learning for the majority of teachers and students was challenging, but further strengthened the topic not only for those with a specific interest in digital media. Besides the boost in digital skills for students and teachers (and most likely for universities as institutions as well), it has become even more obvious that teaching in higher education should support active learner-centered learning, especially for online settings. The purposeful and intentional use of technologies to allow for adaptive and fair learning opportunities in higher education is of ongoing and even growing importance. It is upon teachers to successfully implement online tools into their teaching and to develop teaching and learning arrangements with tools that serve a transparent purpose and also do not neglect student interactions with teachers, as well as with fellow students and with content. With asynchronous teaching formats in particular, we conclude that teachers need to put extra effort into providing sufficient opportunities for students to interact not only with the learning content but also with the teacher and their fellow students. Online settings of teaching and learning hold potential, not only for self-pacing studying, but also for flipped learning arrangements, adaptivity for individual needs, cooperative tasks like wikis or blogs and for automated assessments. All of this should be accompanied by continuous support, not only for technical issues but also for quality teaching and learning in online environments. Therefore, teachers need to be empowered to make the most of digital advances ( OECD, 2020) while having enough room to autonomously make their own decisions and relate to others in this process ( Moorhouse and Kohnke, 2021).

Data Availability Statement

The datasets presented in this article are not readily available because they resulted from the extraordinary evaluation of online teaching at Goethe university which was developed by a joint group of researchers and officials; for further use of the dataset, consent of the group has to be obtained. Requests to access the datasets should be directed to the authors.

Ethics Statement

The studies involving human participants were reviewed and approved by the data security officer of Goethe university Frankfurt. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

SF, JM, and SS contributed to conception and design of the study and wrote sections of the manuscript. SF and JM performed the statistical analysis. All authors contributed to manuscript revision, read, and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We would like to thank the team, namely (in alphabetical order), Michael Eichhorn, Nicola Meier, Dr. Philipp Nolden, Lena Opitz, Jana Niemeyer, and Prof. Dr. Alexander Tillmann for their collaboration in designing the study and for discussing obtained results.

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Frontiers | Impact of Synchronous and Asynchronous Settings of Online Teaching and Learning in Higher Education on Students' Learning Experience During COVID-19

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The sudden impact of the COVID-19 pandemic challenged universities to provide students with online teaching and learning settings that were both immediately ...